Emoji word sense disambiguation

ABSTRACT

The present disclosure generally relates to systems and processes for emoji word sense disambiguation. In one example process, a word sequence is received. A word-level feature representation is determined for each word of the word sequence and a global semantic representation for the word sequence is determined. For a first word of the word sequence, an attention coefficient is determined based on a congruence between the word-level feature representation of the first word and the global semantic representation for the word sequence. The word-level feature representation of the first word is adjusted based on the attention coefficient. An emoji likelihood is determined based on the adjusted word-level feature representation of the first word. In accordance with the emoji likelihood satisfying one or more criteria, an emoji character corresponding to the first word is presented for display.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Ser. No. 62/506,989, filed on May 16, 2017, entitled “EMOJI WORD SENSE DISAMBIGUATION,” which is hereby incorporated by reference in its entirety for all purposes.

FIELD

The present disclosure relates generally to emoji prediction, and more specifically to techniques for emoji word sense disambiguation.

BACKGROUND

Emojis are pictorial symbols used in informal communication channels like electronic messages and Web pages. They can be clustered into two broad categories: ideograms and smileys. Ideograms are pictures of concrete objects like a telephone (

), a bicycle (

), or a rose (

). Smileys are stylized faces intended to convey a particular mood or emotion, such as happiness (

) or sadness (

). In some text input interfaces, ideograms can be suggested as lighthearted entries to replace or supplement actual word entries. For example, if the user enters the word “telephone,” the corresponding ideogram (

) can be predicted and suggested to replace or supplement the word entry. However, conventional deterministic approaches to predict relevant ideograms can be unsophisticated and inaccurate. These approaches can result in the prediction and suggestion of ideograms that are inappropriate given the context of the entered text.

BRIEF SUMMARY

Systems and processes for emoji word sense disambiguation (EWSD) are provided. In one example process, a natural language input comprising a word sequence is received. For each respective word of the word sequence, a feature representation of the respective word is determined based on a left word context for the respective word and the right word context for the respective word. A global semantic representation for the word sequence is determined based on the left word context and the right word context for each respective word of the word sequence. For each respective word of the word sequence that corresponds to a predefined word of a predefined emoji character, a respective attention coefficient is determined based on a congruence between the feature representation of the respective word and the global semantic representation for the word sequence. The feature representation of the respective word is adjusted based on the determined respective attention coefficient. A respective emoji likelihood is determined based on the adjusted feature representation of the respective word. In accordance with the respective emoji likelihood satisfying one or more criteria, an emoji character corresponding to the respective word is caused to be presented for display.

Determining a feature representation of the respective word based on a left word context for the respective word and the right word context for the respective word can improve the accuracy and robustness of emoji character prediction. In particular, it can enable the surrounding context of the respective word to be accounted for, which can be advantageous for accurately disambiguating the semantic meaning of the respective word. This can enable better interpretation of natural language input and allow for the electronic device to operate with greater accuracy and reliability when predicting emoji characters.

Determining the global semantic representation for the word sequence based on the left word context and the right word context for each respective word of the word sequence can also improve the accuracy and robustness of emoji character prediction. In particular, it can serve to distill down the word-level semantic information in the left word context and the right word context to obtain the overall relevant topic of discourse for the word sequence as a whole. The global semantic representation can thus serve as a suitable reference point to enable the EWSD process to focus more on words in the word sequence that are particularly salient to the overall topic and, as a result, particularly relevant for predicting emoji characters. This can enable better interpretation of natural language input and allow for the electronic device to operate with greater accuracy and reliability when predicting emoji characters.

Determining the attention coefficient for the respective word based on a congruence between the feature representation of the respective word and the global semantic representation for the word sequence can also improve the accuracy and robustness of emoji character prediction. In particular, it can enable the EWSD process to quantify the salience of the respective word with respect to the overall topic of the word sequence and thus, its relevance for predicting emoji characters. As a result, a word that is less salient can contribute less to determining the emoji likelihood, whereas a word that is more salient can contribute more to determining the emoji likelihood. This can enable better interpretation of natural language input and allow for the electronic device to operate with greater accuracy and reliability when predicting emoji characters.

Executable instructions for performing these functions are, optionally, included in a non-transitory computer-readable storage medium or other computer program product configured for execution by one or more processors. Executable instructions for performing these functions are, optionally, included in a transitory computer-readable storage medium or other computer program product configured for execution by one or more processors.

DESCRIPTION OF THE FIGURES

For a better understanding of the various described embodiments, reference should be made to the Description of Embodiments below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures.

FIG. 1A is a block diagram illustrating a portable multifunction device with a touch-sensitive display in accordance with some embodiments.

FIG. 1B is a block diagram illustrating exemplary components for event handling in accordance with some embodiments.

FIG. 2 illustrates a portable multifunction device having a touch screen in accordance with some embodiments.

FIG. 3 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments.

FIG. 4A illustrates an exemplary user interface for a menu of applications on a portable multifunction device in accordance with some embodiments.

FIG. 4B illustrates an exemplary user interface for a multifunction device with a touch-sensitive surface that is separate from the display in accordance with some embodiments.

FIG. 5A illustrates a personal electronic device in accordance with some embodiments.

FIG. 5B is a block diagram illustrating a personal electronic device in accordance with some embodiments.

FIG. 6 illustrates an exemplary block diagram of a text prediction module in accordance with some embodiments.

FIGS. 7A-B illustrate an emoji word sense disambiguation model in accordance with some embodiments.

FIG. 8 illustrates an emoji word sense disambiguation model in accordance with some embodiments.

FIGS. 9A-B are flow diagrams illustrating a process for emoji word sense disambiguation in accordance with some embodiments.

DESCRIPTION OF EMBODIMENTS

The following description sets forth exemplary methods, parameters, and the like. It should be recognized, however, that such description is not intended as a limitation on the scope of the present disclosure but is instead provided as a description of exemplary embodiments.

In some conventional text prediction solutions, emojis are suggested simply based on detecting predefined trigger words in the input text without consideration of context. For example, if the predefined trigger word “telephone” is detected in an input text, the ideogram “

” can be automatically predicted and suggested to the user. However, for trigger words having multiple meanings (e.g., homographs), such conventional text prediction solutions would suggest corresponding ideograms regardless of the intended semantic meaning of the trigger words in the input text. For example, if the text input “The past and the future are tiny matters compared to the present” were provided, such conventional text prediction solutions would still suggest the ideogram “

” for the trigger word “present” even though the semantic meaning of “present” in the context of the text input relates to a time period rather than a gift. This makes the text prediction solution appear unpolished and can negatively impacts user experience. In accordance with various techniques described herein, emoji word sense disambiguation (EWSD) can be performed to enable the suggested emoji to be more likely congruent with the intended semantic meaning of the corresponding trigger word.

In accordance with some examples described herein, EWSD can be implemented by accounting for the context before and/or after the trigger word. To illustrate, consider the following exemplary natural language inputs:

-   -   Example A: “For my birthday present, what I'd really like is an         Apple Watch!”     -   Example B: “Regarding the best present, what I'd really like is         an Apple Watch for my birthday!”     -   Example C: “At present, emojis are not being suggested based on         context.”     -   Example D: “The past and the future are tiny matters compared to         the present.”

Regarding Examples A and C, the context immediately prior to the trigger word “present” can be helpful for determining the intended semantic meaning of the trigger word. Thus, based on the immediate prior context, it can be determined that the corresponding ideogram “

” should be suggested for the text input of Example A.

Regarding Example B, the context after the trigger word “present” can be helpful for determining the intended semantic meaning of the trigger word. Thus, based on the subsequent context, it can be determined that the corresponding ideogram “

” should not be suggested for the text input of Example B.

Regarding Example D, conventional text prediction solutions may be unable to utilize the context immediately prior to the trigger word “present” to disambiguate its intended semantic meaning. However, the EWSD techniques described herein can exploit more distant prior context (e.g., based on the phrase “past and future”) to determine the intended semantic meaning of the trigger word and recognize that the ideogram “

” should not be suggested for the text input of Example D.

As described in greater detail below, EWSD is performed by taking the context of the text input into account using bi-directional long short-term memory (LSTM) recurrent neural networks (RNN). The use of bi-directional RNNs (bi-directional LSTM networks) is particularly desirable because it has the ability to encapsulate the entire context available in the text input, rather than only the context within a small sliding window centered around each potential trigger word. This approach therefore makes emoji prediction more robust. Further, in accordance with some examples described herein, EWSD is further performed using an attention mechanism to improve the sensitivity of considering words that are particularly relevant to the semantic meaning of the trigger word. This can be desirable in cases where a word that is particularly relevant to the semantic meaning of a trigger word is separated from the trigger word by many inconsequential words. For example, in the text input of Example B, the word “birthday” is particularly relevant to the semantic meaning of the trigger word “present,” but is separate from the trigger word by a long string of inconsequential words (“what I'd really like is an . . . for my”). Therefore, when considering the context, this long string of inconsequential words can dilute the relevance of the word “birthday,” which causes the determination of the proper semantic meaning of the trigger word to be less robust. The use of an attention mechanism enables the word “birthday” to have more bearing than other words on predicting and suggesting the corresponding ideogram “

.” This further improves the robustness of emoji prediction.

In an example EWSD process described herein, a natural language input comprising a word sequence is received. For each respective word of the word sequence, a feature representation of the respective word is determined (e.g., using a bi-directional LSTM network) based on a left word context for the respective word and the right word context for the respective word. A global semantic representation for the word sequence is determined based on the left word context and the right word context for each respective word of the word sequence. For each respective word of the word sequence that corresponds to a predefined word of a predefined emoji character, a respective attention coefficient is determined (e.g., using an attention mechanism) based on a congruence between the feature representation of the respective word and the global semantic representation for the word sequence. The feature representation of the respective word is adjusted based on the determined respective attention coefficient. A respective emoji likelihood is determined based on the adjusted feature representation of the respective word. In accordance with the respective emoji likelihood satisfying one or more criteria, an emoji character corresponding to the respective word is caused to be presented for display.

Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first text input could be termed a second text input and, similarly, a second text input could be termed a first text input, without departing from the scope of the various described embodiments. The first text input and the second text input are both text inputs, but they are not the same text input.

The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

The term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.

Embodiments of electronic devices, user interfaces for such devices, and associated processes for using such devices are described. In some embodiments, the device is a portable communications device, such as a mobile telephone, that also contains other functions, such as PDA and/or music player functions. Exemplary embodiments of portable multifunction devices include, without limitation, the iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other portable electronic devices, such as laptops or tablet computers with touch-sensitive surfaces (e.g., touch screen displays and/or touchpads), are, optionally, used. It should also be understood that, in some embodiments, the device is not a portable communications device, but is a desktop computer with a touch-sensitive surface (e.g., a touch screen display and/or a touchpad).

In the discussion that follows, an electronic device that includes a display and a touch-sensitive surface is described. It should be understood, however, that the electronic device optionally includes one or more other physical user-interface devices, such as a physical keyboard, a mouse, and/or a joystick.

The device typically supports a variety of applications, such as one or more of the following: a drawing application, a presentation application, a word processing application, a website creation application, a disk authoring application, a spreadsheet application, a gaming application, a telephone application, a video conferencing application, an e-mail application, an instant messaging application, a workout support application, a photo management application, a digital camera application, a digital video camera application, a web browsing application, a digital music player application, and/or a digital video player application.

The various applications that are executed on the device optionally use at least one common physical user-interface device, such as the touch-sensitive surface. One or more functions of the touch-sensitive surface as well as corresponding information displayed on the device are, optionally, adjusted and/or varied from one application to the next and/or within a respective application. In this way, a common physical architecture (such as the touch-sensitive surface) of the device optionally supports the variety of applications with user interfaces that are intuitive and transparent to the user.

Attention is now directed toward embodiments of portable devices with touch-sensitive displays. FIG. 1A is a block diagram illustrating portable multifunction device 100 with touch-sensitive display system 112 in accordance with some embodiments. Touch-sensitive display 112 is sometimes called a “touch screen” for convenience and is sometimes known as or called a “touch-sensitive display system.” Device 100 includes memory 102 (which optionally includes one or more computer-readable storage mediums), memory controller 122, one or more processing units (CPUs) 120, peripherals interface 118, RF circuitry 108, audio circuitry 110, speaker 111, microphone 113, input/output (I/O) subsystem 106, other input control devices 116, and external port 124. Device 100 optionally includes one or more optical sensors 164. Device 100 optionally includes one or more contact intensity sensors 165 for detecting intensity of contacts on device 100 (e.g., a touch-sensitive surface such as touch-sensitive display system 112 of device 100). Device 100 optionally includes one or more tactile output generators 167 for generating tactile outputs on device 100 (e.g., generating tactile outputs on a touch-sensitive surface such as touch-sensitive display system 112 of device 100 or touchpad 355 of device 300). These components optionally communicate over one or more communication buses or signal lines 103.

As used in the specification and claims, the term “intensity” of a contact on a touch-sensitive surface refers to the force or pressure (force per unit area) of a contact (e.g., a finger contact) on the touch-sensitive surface, or to a substitute (proxy) for the force or pressure of a contact on the touch-sensitive surface. The intensity of a contact has a range of values that includes at least four distinct values and more typically includes hundreds of distinct values (e.g., at least 256). Intensity of a contact is, optionally, determined (or measured) using various approaches and various sensors or combinations of sensors. For example, one or more force sensors underneath or adjacent to the touch-sensitive surface are, optionally, used to measure force at various points on the touch-sensitive surface. In some implementations, force measurements from multiple force sensors are combined (e.g., a weighted average) to determine an estimated force of a contact. Similarly, a pressure-sensitive tip of a stylus is, optionally, used to determine a pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and/or changes thereto, the capacitance of the touch-sensitive surface proximate to the contact and/or changes thereto, and/or the resistance of the touch-sensitive surface proximate to the contact and/or changes thereto are, optionally, used as a substitute for the force or pressure of the contact on the touch-sensitive surface. In some implementations, the substitute measurements for contact force or pressure are used directly to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurements). In some implementations, the substitute measurements for contact force or pressure are converted to an estimated force or pressure, and the estimated force or pressure is used to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of a contact as an attribute of a user input allows for user access to additional device functionality that may otherwise not be accessible by the user on a reduced-size device with limited real estate for displaying affordances (e.g., on a touch-sensitive display) and/or receiving user input (e.g., via a touch-sensitive display, a touch-sensitive surface, or a physical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output” refers to physical displacement of a device relative to a previous position of the device, physical displacement of a component (e.g., a touch-sensitive surface) of a device relative to another component (e.g., housing) of the device, or displacement of the component relative to a center of mass of the device that will be detected by a user with the user's sense of touch. For example, in situations where the device or the component of the device is in contact with a surface of a user that is sensitive to touch (e.g., a finger, palm, or other part of a user's hand), the tactile output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in physical characteristics of the device or the component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or trackpad) is, optionally, interpreted by the user as a “down click” or “up click” of a physical actuator button. In some cases, a user will feel a tactile sensation such as an “down click” or “up click” even when there is no movement of a physical actuator button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user's movements. As another example, movement of the touch-sensitive surface is, optionally, interpreted or sensed by the user as “roughness” of the touch-sensitive surface, even when there is no change in smoothness of the touch-sensitive surface. While such interpretations of touch by a user will be subject to the individualized sensory perceptions of the user, there are many sensory perceptions of touch that are common to a large majority of users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., an “up click,” a “down click,” “roughness”), unless otherwise stated, the generated tactile output corresponds to physical displacement of the device or a component thereof that will generate the described sensory perception for a typical (or average) user.

It should be appreciated that device 100 is only one example of a portable multifunction device, and that device 100 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown in FIG. 1A are implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application-specific integrated circuits.

Memory 102 optionally includes high-speed random access memory and optionally also includes non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller 122 optionally controls access to memory 102 by other components of device 100.

Peripherals interface 118 can be used to couple input and output peripherals of the device to CPU 120 and memory 102. The one or more processors 120 run or execute various software programs and/or sets of instructions stored in memory 102 to perform various functions for device 100 and to process data. In some embodiments, peripherals interface 118, CPU 120, and memory controller 122 are, optionally, implemented on a single chip, such as chip 104. In some other embodiments, they are, optionally, implemented on separate chips.

RF (radio frequency) circuitry 108 receives and sends RF signals, also called electromagnetic signals. RF circuitry 108 converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry 108 optionally includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry 108 optionally communicates with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The RF circuitry 108 optionally includes well-known circuitry for detecting near field communication (NFC) fields, such as by a short-range communication radio. The wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, and/or IEEE 802.11ac), voice over Internet Protocol (VoW), Wi-MAX, a protocol for e-mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document.

Audio circuitry 110, speaker 111, and microphone 113 provide an audio interface between a user and device 100. Audio circuitry 110 receives audio data from peripherals interface 118, converts the audio data to an electrical signal, and transmits the electrical signal to speaker 111. Speaker 111 converts the electrical signal to human-audible sound waves. Audio circuitry 110 also receives electrical signals converted by microphone 113 from sound waves. Audio circuitry 110 converts the electrical signal to audio data and transmits the audio data to peripherals interface 118 for processing. Audio data is, optionally, retrieved from and/or transmitted to memory 102 and/or RF circuitry 108 by peripherals interface 118. In some embodiments, audio circuitry 110 also includes a headset jack (e.g., 212, FIG. 2). The headset jack provides an interface between audio circuitry 110 and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone).

I/O subsystem 106 couples input/output peripherals on device 100, such as touch screen 112 and other input control devices 116, to peripherals interface 118. I/O subsystem 106 optionally includes display controller 156, optical sensor controller 158, intensity sensor controller 159, haptic feedback controller 161, and one or more input controllers 160 for other input or control devices. The one or more input controllers 160 receive/send electrical signals from/to other input control devices 116. The other input control devices 116 optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s) 160 are, optionally, coupled to any (or none) of the following: a keyboard, an infrared port, a USB port, and a pointer device such as a mouse. The one or more buttons (e.g., 208, FIG. 2) optionally include an up/down button for volume control of speaker 111 and/or microphone 113. The one or more buttons optionally include a push button (e.g., 206, FIG. 2).

A quick press of the push button optionally disengages a lock of touch screen 112 or optionally begins a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g., 206) optionally turns power to device 100 on or off. The functionality of one or more of the buttons are, optionally, user-customizable. Touch screen 112 is used to implement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 112 provides an input interface and an output interface between the device and a user. Display controller 156 receives and/or sends electrical signals from/to touch screen 112. Touch screen 112 displays visual output to the user. The visual output optionally includes graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output optionally corresponds to user-interface objects.

Touch screen 112 has a touch-sensitive surface, sensor, or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen 112 and display controller 156 (along with any associated modules and/or sets of instructions in memory 102) detect contact (and any movement or breaking of the contact) on touch screen 112 and convert the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web pages, or images) that are displayed on touch screen 112. In an exemplary embodiment, a point of contact between touch screen 112 and the user corresponds to a finger of the user.

Touch screen 112 optionally uses LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies are used in other embodiments. Touch screen 112 and display controller 156 optionally detect contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen 112. In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 112 is, optionally, analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen 112 displays visual output from device 100, whereas touch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 112 is described in the following applications: (1) U.S. patent application Ser. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. patent application Ser. No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30, 2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patent application Ser. No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patent application Ser. No. 11/228,758, “Virtual Input Device Placement On A Touch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patent application Ser. No. 11/228,700, “Operation Of A Computer With A Touch Screen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser. No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No. 11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. All of these applications are incorporated by reference herein in their entirety.

Touch screen 112 optionally has a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user optionally makes contact with touch screen 112 using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen. In some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 100 optionally includes a touchpad (not shown) for activating or deactivating particular functions. In some embodiments, the touchpad is a touch-sensitive area of the device that, unlike the touch screen, does not display visual output. The touchpad is, optionally, a touch-sensitive surface that is separate from touch screen 112 or an extension of the touch-sensitive surface formed by the touch screen.

Device 100 also includes power system 162 for powering the various components. Power system 162 optionally includes a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices.

Device 100 optionally also includes one or more optical sensors 164. FIG. 1A shows an optical sensor coupled to optical sensor controller 158 in I/O subsystem 106. Optical sensor 164 optionally includes charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor 164 receives light from the environment, projected through one or more lenses, and converts the light to data representing an image. In conjunction with imaging module 143 (also called a camera module), optical sensor 164 optionally captures still images or video. In some embodiments, an optical sensor is located on the back of device 100, opposite touch screen display 112 on the front of the device so that the touch screen display is enabled for use as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user's image is, optionally, obtained for video conferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor 164 can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor 164 is used along with the touch screen display for both video conferencing and still and/or video image acquisition.

Device 100 optionally also includes one or more contact intensity sensors 165. FIG. 1A shows a contact intensity sensor coupled to intensity sensor controller 159 in I/O subsystem 106. Contact intensity sensor 165 optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electric force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other intensity sensors (e.g., sensors used to measure the force (or pressure) of a contact on a touch-sensitive surface). Contact intensity sensor 165 receives contact intensity information (e.g., pressure information or a proxy for pressure information) from the environment. In some embodiments, at least one contact intensity sensor is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system 112). In some embodiments, at least one contact intensity sensor is located on the back of device 100, opposite touch screen display 112, which is located on the front of device 100.

Device 100 optionally also includes one or more proximity sensors 166. FIG. 1A shows proximity sensor 166 coupled to peripherals interface 118. Alternately, proximity sensor 166 is, optionally, coupled to input controller 160 in I/O subsystem 106. Proximity sensor 166 optionally performs as described in U.S. patent application Ser. No. 11/241,839, “Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “Proximity Detector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and Ser. No. 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen 112 when the multifunction device is placed near the user's ear (e.g., when the user is making a phone call).

Device 100 optionally also includes one or more tactile output generators 167. FIG. 1A shows a tactile output generator coupled to haptic feedback controller 161 in I/O subsystem 106. Tactile output generator 167 optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor 165 receives tactile feedback generation instructions from haptic feedback module 133 and generates tactile outputs on device 100 that are capable of being sensed by a user of device 100. In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system 112) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device 100) or laterally (e.g., back and forth in the same plane as a surface of device 100). In some embodiments, at least one tactile output generator sensor is located on the back of device 100, opposite touch screen display 112, which is located on the front of device 100.

Device 100 optionally also includes one or more accelerometers 168. FIG. 1A shows accelerometer 168 coupled to peripherals interface 118. Alternately, accelerometer 168 is, optionally, coupled to an input controller 160 in I/O subsystem 106. Accelerometer 168 optionally performs as described in U.S. Patent Publication No. 20050190059, “Acceleration-based Theft Detection System for Portable Electronic Devices,” and U.S. Patent Publication No. 20060017692, “Methods And Apparatuses For Operating A Portable Device Based On An Accelerometer,” both of which are incorporated by reference herein in their entirety. In some embodiments, information is displayed on the touch screen display in a portrait view or a landscape view based on an analysis of data received from the one or more accelerometers. Device 100 optionally includes, in addition to accelerometer(s) 168, a magnetometer (not shown) and a GPS (or GLONASS or other global navigation system) receiver (not shown) for obtaining information concerning the location and orientation (e.g., portrait or landscape) of device 100.

In some embodiments, the software components stored in memory 102 include operating system 126, communication module (or set of instructions) 128, contact/motion module (or set of instructions) 130, graphics module (or set of instructions) 132, text input module (or set of instructions) 134, Global Positioning System (GPS) module (or set of instructions) 135, and applications (or sets of instructions) 136. Furthermore, in some embodiments, memory 102 (FIG. 1A) or 370 (FIG. 3) stores device/global internal state 157, as shown in FIGS. 1A and 3. Device/global internal state 157 includes one or more of: active application state, indicating which applications, if any, are currently active; display state, indicating what applications, views or other information occupy various regions of touch screen display 112; sensor state, including information obtained from the device's various sensors and input control devices 116; and location information concerning the device's location and/or attitude.

Operating system 126 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.

Communication module 128 facilitates communication with other devices over one or more external ports 124 and also includes various software components for handling data received by RF circuitry 108 and/or external port 124. External port 124 (e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin (e.g., 30-pin) connector that is the same as, or similar to and/or compatible with, the 30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 130 optionally detects contact with touch screen 112 (in conjunction with display controller 156) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module 130 includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining an intensity of the contact (e.g., the force or pressure of the contact or a substitute for the force or pressure of the contact), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module 130 receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, optionally includes determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations are, optionally, applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module 130 and display controller 156 detect contact on a touchpad.

In some embodiments, contact/motion module 130 uses a set of one or more intensity thresholds to determine whether an operation has been performed by a user (e.g., to determine whether a user has “clicked” on an icon). In some embodiments, at least a subset of the intensity thresholds are determined in accordance with software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of particular physical actuators and can be adjusted without changing the physical hardware of device 100). For example, a mouse “click” threshold of a trackpad or touch screen display can be set to any of a large range of predefined threshold values without changing the trackpad or touch screen display hardware. Additionally, in some implementations, a user of the device is provided with software settings for adjusting one or more of the set of intensity thresholds (e.g., by adjusting individual intensity thresholds and/or by adjusting a plurality of intensity thresholds at once with a system-level click “intensity” parameter).

Contact/motion module 130 optionally detects a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns (e.g., different motions, timings, and/or intensities of detected contacts). Thus, a gesture is, optionally, detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes detecting a finger-down event followed by detecting a finger-up (liftoff) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger-down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (liftoff) event.

Graphics module 132 includes various known software components for rendering and displaying graphics on touch screen 112 or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual property) of graphics that are displayed. As used herein, the term “graphics” includes any object that can be displayed to a user, including, without limitation, text, web pages, icons (such as user-interface objects including soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 132 stores data representing graphics to be used. Each graphic is, optionally, assigned a corresponding code. Graphics module 132 receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller 156.

Haptic feedback module 133 includes various software components for generating instructions used by tactile output generator(s) 167 to produce tactile outputs at one or more locations on device 100 in response to user interactions with device 100.

Text input module 134, which is, optionally, a component of graphics module 132, provides soft keyboards for entering text in various applications (e.g., contacts 137, e-mail 140, IM 141, browser 147, and any other application that needs text input).

GPS module 135 determines the location of the device and provides this information for use in various applications (e.g., to telephone 138 for use in location-based dialing; to camera 143 as picture/video metadata; and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets).

Applications 136 optionally include the following modules (or sets of instructions), or a subset or superset thereof:

-   -   Contacts module 137 (sometimes called an address book or contact         list);     -   Telephone module 138;     -   Video conference module 139;     -   E-mail client module 140;     -   Instant messaging (IM) module 141;     -   Workout support module 142;     -   Camera module 143 for still and/or video images;     -   Image management module 144;     -   Video player module;     -   Music player module;     -   Browser module 147;     -   Calendar module 148;     -   Widget modules 149, which optionally include one or more of:         weather widget 149-1, stocks widget 149-2, calculator widget         149-3, alarm clock widget 149-4, dictionary widget 149-5, and         other widgets obtained by the user, as well as user-created         widgets 149-6;     -   Widget creator module 150 for making user-created widgets 149-6;     -   Search module 151;     -   Video and music player module 152, which merges video player         module and music player module;     -   Notes module 153;     -   Map module 154; and/or     -   Online video module 155.

Examples of other applications 136 that are, optionally, stored in memory 102 include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication.

In conjunction with touch screen 112, display controller 156, contact/motion module 130, graphics module 132, and text input module 134, contacts module 137 are, optionally, used to manage an address book or contact list (e.g., stored in application internal state 192 of contacts module 137 in memory 102 or memory 370), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone 138, video conference module 139, e-mail 140, or IM 141; and so forth.

In conjunction with RF circuitry 108, audio circuitry 110, speaker 111, microphone 113, touch screen 112, display controller 156, contact/motion module 130, graphics module 132, and text input module 134, telephone module 138 are optionally, used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in contacts module 137, modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As noted above, the wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies.

In conjunction with RF circuitry 108, audio circuitry 110, speaker 111, microphone 113, touch screen 112, display controller 156, optical sensor 164, optical sensor controller 158, contact/motion module 130, graphics module 132, text input module 134, contacts module 137, and telephone module 138, video conference module 139 includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 108, touch screen 112, display controller 156, contact/motion module 130, graphics module 132, and text input module 134, e-mail client module 140 includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module 144, e-mail client module 140 makes it very easy to create and send e-mails with still or video images taken with camera module 143.

In conjunction with RF circuitry 108, touch screen 112, display controller 156, contact/motion module 130, graphics module 132, and text input module 134, the instant messaging module 141 includes executable instructions to enter a sequence of characters corresponding to an instant message, to modify previously entered characters, to transmit a respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages, and to view received instant messages. In some embodiments, transmitted and/or received instant messages optionally include graphics, photos, audio files, video files and/or other attachments as are supported in an MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS).

In conjunction with RF circuitry 108, touch screen 112, display controller 156, contact/motion module 130, graphics module 132, text input module 134, GPS module 135, map module 154, and music player module, workout support module 142 includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store, and transmit workout data.

In conjunction with touch screen 112, display controller 156, optical sensor(s) 164, optical sensor controller 158, contact/motion module 130, graphics module 132, and image management module 144, camera module 143 includes executable instructions to capture still images or video (including a video stream) and store them into memory 102, modify characteristics of a still image or video, or delete a still image or video from memory 102.

In conjunction with touch screen 112, display controller 156, contact/motion module 130, graphics module 132, text input module 134, and camera module 143, image management module 144 includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images.

In conjunction with RF circuitry 108, touch screen 112, display controller 156, contact/motion module 130, graphics module 132, and text input module 134, browser module 147 includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web pages or portions thereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 108, touch screen 112, display controller 156, contact/motion module 130, graphics module 132, text input module 134, e-mail client module 140, and browser module 147, calendar module 148 includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to-do lists, etc.) in accordance with user instructions.

In conjunction with RF circuitry 108, touch screen 112, display controller 156, contact/motion module 130, graphics module 132, text input module 134, and browser module 147, widget modules 149 are mini-applications that are, optionally, downloaded and used by a user (e.g., weather widget 149-1, stocks widget 149-2, calculator widget 149-3, alarm clock widget 149-4, and dictionary widget 149-5) or created by the user (e.g., user-created widget 149-6). In some embodiments, a widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! Widgets).

In conjunction with RF circuitry 108, touch screen 112, display controller 156, contact/motion module 130, graphics module 132, text input module 134, and browser module 147, the widget creator module 150 are, optionally, used by a user to create widgets (e.g., turning a user-specified portion of a web page into a widget).

In conjunction with touch screen 112, display controller 156, contact/motion module 130, graphics module 132, and text input module 134, search module 151 includes executable instructions to search for text, music, sound, image, video, and/or other files in memory 102 that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions.

In conjunction with touch screen 112, display controller 156, contact/motion module 130, graphics module 132, audio circuitry 110, speaker 111, RF circuitry 108, and browser module 147, video and music player module 152 includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen 112 or on an external, connected display via external port 124). In some embodiments, device 100 optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.).

In conjunction with touch screen 112, display controller 156, contact/motion module 130, graphics module 132, and text input module 134, notes module 153 includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions.

In conjunction with RF circuitry 108, touch screen 112, display controller 156, contact/motion module 130, graphics module 132, text input module 134, GPS module 135, and browser module 147, map module 154 are, optionally, used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions, data on stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions.

In conjunction with touch screen 112, display controller 156, contact/motion module 130, graphics module 132, audio circuitry 110, speaker 111, RF circuitry 108, text input module 134, e-mail client module 140, and browser module 147, online video module 155 includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port 124), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module 141, rather than e-mail client module 140, is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules are, optionally, combined or otherwise rearranged in various embodiments. For example, video player module is, optionally, combined with music player module into a single module (e.g., video and music player module 152, FIG. 1A). In some embodiments, memory 102 optionally stores a subset of the modules and data structures identified above. Furthermore, memory 102 optionally stores additional modules and data structures not described above.

In some embodiments, device 100 is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device 100, the number of physical input control devices (such as push buttons, dials, and the like) on device 100 is, optionally, reduced.

The predefined set of functions that are performed exclusively through a touch screen and/or a touchpad optionally include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device 100 to a main, home, or root menu from any user interface that is displayed on device 100. In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad.

FIG. 1B is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory 102 (FIG. 1A) or 370 (FIG. 3) includes event sorter 170 (e.g., in operating system 126) and a respective application 136-1 (e.g., any of the aforementioned applications 137-151, 155, 380-390).

Event sorter 170 receives event information and determines the application 136-1 and application view 191 of application 136-1 to which to deliver the event information. Event sorter 170 includes event monitor 171 and event dispatcher module 174. In some embodiments, application 136-1 includes application internal state 192, which indicates the current application view(s) displayed on touch-sensitive display 112 when the application is active or executing. In some embodiments, device/global internal state 157 is used by event sorter 170 to determine which application(s) is (are) currently active, and application internal state 192 is used by event sorter 170 to determine application views 191 to which to deliver event information.

In some embodiments, application internal state 192 includes additional information, such as one or more of: resume information to be used when application 136-1 resumes execution, user interface state information that indicates information being displayed or that is ready for display by application 136-1, a state queue for enabling the user to go back to a prior state or view of application 136-1, and a redo/undo queue of previous actions taken by the user.

Event monitor 171 receives event information from peripherals interface 118. Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display 112, as part of a multi-touch gesture). Peripherals interface 118 transmits information it receives from I/O subsystem 106 or a sensor, such as proximity sensor 166, accelerometer(s) 168, and/or microphone 113 (through audio circuitry 110). Information that peripherals interface 118 receives from I/O subsystem 106 includes information from touch-sensitive display 112 or a touch-sensitive surface.

In some embodiments, event monitor 171 sends requests to the peripherals interface 118 at predetermined intervals. In response, peripherals interface 118 transmits event information. In other embodiments, peripherals interface 118 transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 170 also includes a hit view determination module 172 and/or an active event recognizer determination module 173.

Hit view determination module 172 provides software procedures for determining where a sub-event has taken place within one or more views when touch-sensitive display 112 displays more than one view. Views are made up of controls and other elements that a user can see on the display.

Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected optionally correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected is, optionally, called the hit view, and the set of events that are recognized as proper inputs are, optionally, determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture.

Hit view determination module 172 receives information related to sub-events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module 172 identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module 172, the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view.

Active event recognizer determination module 173 determines which view or views within a view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module 173 determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module 173 determines that all views that include the physical location of a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views.

Event dispatcher module 174 dispatches the event information to an event recognizer (e.g., event recognizer 180). In embodiments including active event recognizer determination module 173, event dispatcher module 174 delivers the event information to an event recognizer determined by active event recognizer determination module 173. In some embodiments, event dispatcher module 174 stores in an event queue the event information, which is retrieved by a respective event receiver 182.

In some embodiments, operating system 126 includes event sorter 170. Alternatively, application 136-1 includes event sorter 170. In yet other embodiments, event sorter 170 is a stand-alone module, or a part of another module stored in memory 102, such as contact/motion module 130.

In some embodiments, application 136-1 includes a plurality of event handlers 190 and one or more application views 191, each of which includes instructions for handling touch events that occur within a respective view of the application's user interface. Each application view 191 of the application 136-1 includes one or more event recognizers 180. Typically, a respective application view 191 includes a plurality of event recognizers 180. In other embodiments, one or more of event recognizers 180 are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application 136-1 inherits methods and other properties. In some embodiments, a respective event handler 190 includes one or more of: data updater 176, object updater 177, GUI updater 178, and/or event data 179 received from event sorter 170. Event handler 190 optionally utilizes or calls data updater 176, object updater 177, or GUI updater 178 to update the application internal state 192. Alternatively, one or more of the application views 191 include one or more respective event handlers 190. Also, in some embodiments, one or more of data updater 176, object updater 177, and GUI updater 178 are included in a respective application view 191.

A respective event recognizer 180 receives event information (e.g., event data 179) from event sorter 170 and identifies an event from the event information. Event recognizer 180 includes event receiver 182 and event comparator 184. In some embodiments, event recognizer 180 also includes at least a subset of: metadata 183, and event delivery instructions 188 (which optionally include sub-event delivery instructions).

Event receiver 182 receives event information from event sorter 170. The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch, the event information optionally also includes speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device attitude) of the device.

Event comparator 184 compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub-event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator 184 includes event definitions 186. Event definitions 186 contain definitions of events (e.g., predefined sequences of sub-events), for example, event 1 (187-1), event 2 (187-2), and others. In some embodiments, sub-events in an event (187) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event 1 (187-1) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event 2 (187-2) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display 112, and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers 190.

In some embodiments, event definition 187 includes a definition of an event for a respective user-interface object. In some embodiments, event comparator 184 performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display 112, when a touch is detected on touch-sensitive display 112, event comparator 184 performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective event handler 190, the event comparator uses the result of the hit test to determine which event handler 190 should be activated. For example, event comparator 184 selects an event handler associated with the sub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (187) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer's event type.

When a respective event recognizer 180 determines that the series of sub-events do not match any of the events in event definitions 186, the respective event recognizer 180 enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 180 includes metadata 183 with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata 183 includes configurable properties, flags, and/or lists that indicate how event recognizers interact, or are enabled to interact, with one another. In some embodiments, metadata 183 includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 180 activates event handler 190 associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer 180 delivers event information associated with the event to event handler 190. Activating an event handler 190 is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer 180 throws a flag associated with the recognized event, and event handler 190 associated with the flag catches the flag and performs a predefined process.

In some embodiments, event delivery instructions 188 include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated with the series of sub-events or with actively involved views receive the event information and perform a predetermined process.

In some embodiments, data updater 176 creates and updates data used in application 136-1. For example, data updater 176 updates the telephone number used in contacts module 137, or stores a video file used in video player module. In some embodiments, object updater 177 creates and updates objects used in application 136-1. For example, object updater 177 creates a new user-interface object or updates the position of a user-interface object. GUI updater 178 updates the GUI. For example, GUI updater 178 prepares display information and sends it to graphics module 132 for display on a touch-sensitive display.

In some embodiments, event handler(s) 190 includes or has access to data updater 176, object updater 177, and GUI updater 178. In some embodiments, data updater 176, object updater 177, and GUI updater 178 are included in a single module of a respective application 136-1 or application view 191. In other embodiments, they are included in two or more software modules.

It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices 100 with input devices, not all of which are initiated on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs; movement of the device; oral instructions; detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub-events which define an event to be recognized.

FIG. 2 illustrates a portable multifunction device 100 having a touch screen 112 in accordance with some embodiments. The touch screen optionally displays one or more graphics within user interface (UI) 200. In this embodiment, as well as others described below, a user is enabled to select one or more of the graphics by making a gesture on the graphics, for example, with one or more fingers 202 (not drawn to scale in the figure) or one or more styluses 203 (not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the gesture optionally includes one or more taps, one or more swipes (from left to right, right to left, upward and/or downward), and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device 100. In some implementations or circumstances, inadvertent contact with a graphic does not select the graphic. For example, a swipe gesture that sweeps over an application icon optionally does not select the corresponding application when the gesture corresponding to selection is a tap.

Device 100 optionally also include one or more physical buttons, such as “home” or menu button 204. As described previously, menu button 204 is, optionally, used to navigate to any application 136 in a set of applications that are, optionally, executed on device 100. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen 112.

In some embodiments, device 100 includes touch screen 112, menu button 204, push button 206 for powering the device on/off and locking the device, volume adjustment button(s) 208, subscriber identity module (SIM) card slot 210, headset jack 212, and docking/charging external port 124. Push button 206 is, optionally, used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device 100 also accepts verbal input for activation or deactivation of some functions through microphone 113. Device 100 also, optionally, includes one or more contact intensity sensors 165 for detecting intensity of contacts on touch screen 112 and/or one or more tactile output generators 167 for generating tactile outputs for a user of device 100.

FIG. 3 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device 300 need not be portable. In some embodiments, device 300 is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child's learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device 300 typically includes one or more processing units (CPUs) 310, one or more network or other communications interfaces 360, memory 370, and one or more communication buses 320 for interconnecting these components. Communication buses 320 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device 300 includes input/output (I/O) interface 330 comprising display 340, which is typically a touch screen display. I/O interface 330 also optionally includes a keyboard and/or mouse (or other pointing device) 350 and touchpad 355, tactile output generator 357 for generating tactile outputs on device 300 (e.g., similar to tactile output generator(s) 167 described above with reference to FIG. 1A), sensors 359 (e.g., optical, acceleration, proximity, touch-sensitive, and/or contact intensity sensors similar to contact intensity sensor(s) 165 described above with reference to FIG. 1A). Memory 370 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 370 optionally includes one or more storage devices remotely located from CPU(s) 310. In some embodiments, memory 370 stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory 102 of portable multifunction device 100 (FIG. 1A), or a subset thereof. Furthermore, memory 370 optionally stores additional programs, modules, and data structures not present in memory 102 of portable multifunction device 100. For example, memory 370 of device 300 optionally stores drawing module 380, presentation module 382, word processing module 384, website creation module 386, disk authoring module 388, and/or spreadsheet module 390, while memory 102 of portable multifunction device 100 (FIG. 1A) optionally does not store these modules.

Each of the above-identified elements in FIG. 3 is, optionally, stored in one or more of the previously mentioned memory devices. Each of the above-identified modules corresponds to a set of instructions for performing a function described above. The above-identified modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules are, optionally, combined or otherwise rearranged in various embodiments. In some embodiments, memory 370 optionally stores a subset of the modules and data structures identified above. Furthermore, memory 370 optionally stores additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces that are, optionally, implemented on, for example, portable multifunction device 100.

FIG. 4A illustrates an exemplary user interface for a menu of applications on portable multifunction device 100 in accordance with some embodiments. Similar user interfaces are, optionally, implemented on device 300. In some embodiments, user interface 400 includes the following elements, or a subset or superset thereof:

-   -   Signal strength indicator(s) 402 for wireless communication(s),         such as cellular and Wi-Fi signals;     -   Time 404;     -   Bluetooth indicator 405;     -   Battery status indicator 406;     -   Tray 408 with icons for frequently used applications, such as:         -   Icon 416 for telephone module 138, labeled “Phone,” which             optionally includes an indicator 414 of the number of missed             calls or voicemail messages;         -   Icon 418 for e-mail client module 140, labeled “Mail,” which             optionally includes an indicator 410 of the number of unread             e-mails;         -   Icon 420 for browser module 147, labeled “Browser;” and         -   Icon 422 for video and music player module 152, also             referred to as iPod (trademark of Apple Inc.) module 152,             labeled “iPod;” and     -   Icons for other applications, such as:         -   Icon 424 for IM module 141, labeled “Messages;”         -   Icon 426 for calendar module 148, labeled “Calendar;”         -   Icon 428 for image management module 144, labeled “Photos;”         -   Icon 430 for camera module 143, labeled “Camera;”         -   Icon 432 for online video module 155, labeled “Online             Video;”         -   Icon 434 for stocks widget 149-2, labeled “Stocks;”         -   Icon 436 for map module 154, labeled “Maps;”         -   Icon 438 for weather widget 149-1, labeled “Weather;”         -   Icon 440 for alarm clock widget 149-4, labeled “Clock;”         -   Icon 442 for workout support module 142, labeled “Workout             Support;”         -   Icon 444 for notes module 153, labeled “Notes;” and         -   Icon 446 for a settings application or module, labeled             “Settings,” which provides access to settings for device 100             and its various applications 136.

It should be noted that the icon labels illustrated in FIG. 4A are merely exemplary. For example, icon 422 for video and music player module 152 is labeled “Music” or “Music Player.” Other labels are, optionally, used for various application icons. In some embodiments, a label for a respective application icon includes a name of an application corresponding to the respective application icon. In some embodiments, a label for a particular application icon is distinct from a name of an application corresponding to the particular application icon.

FIG. 4B illustrates an exemplary user interface on a device (e.g., device 300, FIG. 3) with a touch-sensitive surface 451 (e.g., a tablet or touchpad 355, FIG. 3) that is separate from the display 450 (e.g., touch screen display 112). Device 300 also, optionally, includes one or more contact intensity sensors (e.g., one or more of sensors 359) for detecting intensity of contacts on touch-sensitive surface 451 and/or one or more tactile output generators 357 for generating tactile outputs for a user of device 300.

Although some of the examples that follow will be given with reference to inputs on touch screen display 112 (where the touch-sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in FIG. 4B. In some embodiments, the touch-sensitive surface (e.g., 451 in FIG. 4B) has a primary axis (e.g., 452 in FIG. 4B) that corresponds to a primary axis (e.g., 453 in FIG. 4B) on the display (e.g., 450). In accordance with these embodiments, the device detects contacts (e.g., 460 and 462 in FIG. 4B) with the touch-sensitive surface 451 at locations that correspond to respective locations on the display (e.g., in FIG. 4B, 460 corresponds to 468 and 462 corresponds to 470). In this way, user inputs (e.g., contacts 460 and 462, and movements thereof) detected by the device on the touch-sensitive surface (e.g., 451 in FIG. 4B) are used by the device to manipulate the user interface on the display (e.g., 450 in FIG. 4B) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods are, optionally, used for other user interfaces described herein.

Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a contact) followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is, optionally, replaced with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse and finger contacts are, optionally, used simultaneously.

FIG. 5A illustrates exemplary personal electronic device 500. Device 500 includes body 502. In some embodiments, device 500 can include some or all of the features described with respect to devices 100 and 300 (e.g., FIGS. 1A-4B). In some embodiments, device 500 has touch-sensitive display screen 504, hereafter touch screen 504. Alternatively, or in addition to touch screen 504, device 500 has a display and a touch-sensitive surface. As with devices 100 and 300, in some embodiments, touch screen 504 (or the touch-sensitive surface) optionally includes one or more intensity sensors for detecting intensity of contacts (e.g., touches) being applied. The one or more intensity sensors of touch screen 504 (or the touch-sensitive surface) can provide output data that represents the intensity of touches. The user interface of device 500 can respond to touches based on their intensity, meaning that touches of different intensities can invoke different user interface operations on device 500.

Exemplary techniques for detecting and processing touch intensity are found, for example, in related applications: International Patent Application Serial No. PCT/US2013/040061, titled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application,” filed May 8, 2013, published as WIPO Publication No. WO/2013/169849, and International Patent Application Serial No. PCT/US2013/069483, titled “Device, Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships,” filed Nov. 11, 2013, published as WIPO Publication No. WO/2014/105276, each of which is hereby incorporated by reference in their entirety.

In some embodiments, device 500 has one or more input mechanisms 506 and 508. Input mechanisms 506 and 508, if included, can be physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, device 500 has one or more attachment mechanisms. Such attachment mechanisms, if included, can permit attachment of device 500 with, for example, hats, eyewear, earrings, necklaces, shirts, jackets, bracelets, watch straps, chains, trousers, belts, shoes, purses, backpacks, and so forth. These attachment mechanisms permit device 500 to be worn by a user.

FIG. 5B depicts exemplary personal electronic device 500. In some embodiments, device 500 can include some or all of the components described with respect to FIGS. 1A, 1B, and 3. Device 500 has bus 512 that operatively couples I/O section 514 with one or more computer processors 516 and memory 518. I/O section 514 can be connected to display 504, which can have touch-sensitive component 522 and, optionally, intensity sensor 524 (e.g., contact intensity sensor). In addition, I/O section 514 can be connected with communication unit 530 for receiving application and operating system data, using Wi-Fi, Bluetooth, near field communication (NFC), cellular, and/or other wireless communication techniques. Device 500 can include input mechanisms 506 and/or 508. Input mechanism 506 is, optionally, a rotatable input device or a depressible and rotatable input device, for example. Input mechanism 508 is, optionally, a button, in some examples.

Input mechanism 508 is, optionally, a microphone, in some examples. Personal electronic device 500 optionally includes various sensors, such as GPS sensor 532, accelerometer 534, directional sensor 540 (e.g., compass), gyroscope 536, motion sensor 538, and/or a combination thereof, all of which can be operatively connected to I/O section 514.

Memory 518 of personal electronic device 500 can include one or more non-transitory computer-readable storage mediums, for storing computer-executable instructions, which, when executed by one or more computer processors 516, for example, can cause the computer processors to perform the techniques described below, including process 900 (FIGS. 9A-9B). Personal electronic device 500 is not limited to the components and configuration of FIG. 5B, but can include other or additional components in multiple configurations.

As used here, the term “affordance” refers to a user-interactive graphical user interface object that is, optionally, displayed on the display screen of devices 100, 300, and/or 500 (FIGS. 1, 3, and 5). For example, an image (e.g., icon), a button, and text (e.g., hyperlink) each optionally constitute an affordance.

As used herein, the term “focus selector” refers to an input element that indicates a current part of a user interface with which a user is interacting. In some implementations that include a cursor or other location marker, the cursor acts as a “focus selector” so that when an input (e.g., a press input) is detected on a touch-sensitive surface (e.g., touchpad 355 in FIG. 3 or touch-sensitive surface 451 in FIG. 4B) while the cursor is over a particular user interface element (e.g., a button, window, slider, or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations that include a touch screen display (e.g., touch-sensitive display system 112 in FIG. 1A or touch screen 112 in FIG. 4A) that enables direct interaction with user interface elements on the touch screen display, a detected contact on the touch screen acts as a “focus selector” so that when an input (e.g., a press input by the contact) is detected on the touch screen display at a location of a particular user interface element (e.g., a button, window, slider, or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations, focus is moved from one region of a user interface to another region of the user interface without corresponding movement of a cursor or movement of a contact on a touch screen display (e.g., by using a tab key or arrow keys to move focus from one button to another button); in these implementations, the focus selector moves in accordance with movement of focus between different regions of the user interface. Without regard to the specific form taken by the focus selector, the focus selector is generally the user interface element (or contact on a touch screen display) that is controlled by the user so as to communicate the user's intended interaction with the user interface (e.g., by indicating, to the device, the element of the user interface with which the user is intending to interact). For example, the location of a focus selector (e.g., a cursor, a contact, or a selection box) over a respective button while a press input is detected on the touch-sensitive surface (e.g., a touchpad or touch screen) will indicate that the user is intending to activate the respective button (as opposed to other user interface elements shown on a display of the device).

FIG. 6 illustrates an exemplary schematic block diagram of text prediction module 600 in accordance with some embodiments. In some embodiments, text prediction module 600 is implemented using one or more multifunction devices, including, but not limited to, devices 100 and 300 (FIGS. 1A and 3). In some examples, memory 102 (FIG. 1A) or 370 (FIG. 3) includes text prediction module 600. Text prediction module 600, in some examples, includes lexicons, models, and instructions for performing the various ESWD functionalities described below in process 900. It should be recognized that text prediction module 600 need not be implemented as a separate software program, procedure, or module, and thus, various subsets of the module are, optionally, combined or otherwise rearranged in various embodiments.

As shown in FIG. 6, text prediction module 600 includes text prediction engine 602, lexicon(s) 606, language model(s) 608, and EWSD model(s) 610. Lexicon(s) 606 includes one or more collections of words, phrases, and/or emoji characters. For example, lexicon(s) 606 includes a vocabulary of N words/phrases that is used to encode the words in the text input received at text prediction engine 602. In some examples, lexicon(s) 606 further includes a list of emoji characters. Each emoji character is associated with one or more predefined trigger words of a list of predefined trigger words in lexicon(s) 606. Each predefined trigger word of the list of trigger words maps to a corresponding emoji character of the list of emoji characters (e.g., the trigger word “present” maps to the emoji character “

”). Language model(s) 608 includes one or more statistical language models (e.g., n-gram language models, neural network-based language models, etc.). In particular, the one or more statistical language models are configured to determine the probability of a candidate predicted word given a context in the text input.

EWSD model(s) 610 includes one or more EWSD models (e.g., EWSD model 700 and/or EWSD model 800) that are configured to receive a word sequence of the text input and determine an emoji likelihood for a given word (e.g., trigger word) in the word sequence. The emoji likelihood indicates, for example, the likelihood that the semantic meaning of the given word is congruent with the corresponding emoji character. Thus, the emoji likelihood provides the likelihood that the corresponding emoji character should be presented for the given word. Exemplary EWSD models are described in greater detail below, with reference to FIGS. 7A-7B and 8.

Text prediction module 600 is configured to determine one or more candidate predicted words/emoji characters in accordance with a context, such as the text input received from the user. The one or more candidate predicted words/emoji characters can be applicable to word prediction, word completion, or word correction/replacement applications. As shown in FIG. 6, text input is received by text prediction engine 602. The text input includes, for example, a word sequence. Text prediction engine 602 utilizes lexicon(s) 606, language model(s) 608, and EWSD model(s) 610 to determine a plurality of candidate predicted words/emoji characters given the text input. Additionally, for each candidate predicted word/emoji character of the plurality of candidate predicted words/emoji characters, a likelihood score is determined using language model(s) 608 and/or EWSD model(s) 610. The likelihood score of a candidate predicted word represents the likelihood of the candidate predicted word/emoji given the text input. Text prediction engine 602 outputs the plurality of candidate predicted words/emoji characters and the corresponding likelihood scores. Based on the likelihood scores, the plurality of candidate predicted words/emoji characters can be ranked and the n-best candidate predicted words/emoji characters can be presented for display.

FIGS. 7A-B illustrate EWSD model 700 in accordance with some embodiments. In particular, FIG. 7A illustrates a compact representation of EWSD model 700 and FIG. 7B illustrates an unfolded representation of EWSD model 700 for each time step (1≤t≤T). A word w_(t) of the word sequence is processed through EWSD model 700 at a respective time step t. In this example, EWSD model 700 includes a neural network (e.g., LSTM network or RNN) that can serve to determine whether a corresponding emoji character should be presented for a given word of a word sequence. Specifically, in this example, EWSD model 700 has a bi-directional RNN architecture. EWSD model 700 can be implemented using one or more multifunction devices including, but not limited to, devices 100 and 300 (FIGS. 1A and 3). In some examples, EWSD model(s) 610 of text prediction module 600 includes EWSD model 700.

As shown, EWSD model 700 comprises a cascade of two stages: a word-level feature extraction stage 760 for determining a word-level feature representation h_(t) of each word in a word sequence followed by an emoji labeling stage 770 for determining an emoji likelihood y_(t) for each word in the word sequence. In this example, word-level feature extraction stage 760 comprises a single bi-directional LSTM stage. It should be recognized that, in other examples, word-level feature extraction stage 760 can comprise a stack of two or more bi-directional LSTM stages.

EWSD model 700 includes multiple layers. For instance, EWSD model 700 includes input layer 710, one or more hidden layers 720, and output layer 730. In this example, EWSD model 700 includes a single hidden layer 720. It will be appreciated, however, that in other examples, EWSD model 700 can include one or more additional hidden layers 720.

Each layer of EWSD model 700 includes any number of units. A layer, for instance, comprises a single unit or multiple units. These units, which in some examples are referred to as dimensions, neurons, or nodes (e.g., context nodes), operate as the computational elements of EWSD model 700. As illustrated, input layer 710 includes current word unit 712, previous left context unit 714, and subsequent right context unit 716. Hidden layer 720 includes left context unit 722 and right context unit 724. Output layer 730 includes emoji label unit 732. Units of EWSD model 700 are interconnected using connections. Connections are unidirectional or bidirectional, and are further associated with a respective weight value. Each weight value specifies a strength of the corresponding connection and, accordingly, the relative influence of the value provided via the connection. As illustrated, previous left context unit 714 and current word unit 712 are each connected to left context unit 722. Subsequent right context unit 716 and current word unit 712 are connected to right context unit 724. In addition, left context unit 722 is connected to previous left context unit 714 via left recurrent connection 740, and right context unit 724 is connected to subsequent right context unit 716 via right recurrent connection 750. Further, left context unit 722 and right context unit 724 are connected to emoji label unit 732.

In operation, current word unit 712 of input layer 710 receives a current word w_(t) of a word sequence (e.g., a sentence or string of words input by a user) and provides values (e.g., vector representation) corresponding to the current word to the units of hidden layer 720 via connections interconnected between the units of the input layer 710 and the units of hidden layer 720. The current word unit 712 of the input layer 710 is connected to left context unit 722 and right context unit 724 of the hidden layer 720 and provides a current word w_(t) to left context unit 722 and right context unit 724. Generally, w_(t) is a representation (e.g., vector representation) of the current word received at current time step t and is provided by encoding the current word using 1-of-N encoding. Accordingly, w_(t) has a dimension equal to N, where N is the size of the word vocabulary (e.g., word vocabulary of lexicon(s) 606). In some examples, input layer 710 encodes a plurality of words to provide w_(t). The word sequence {w_(t)}, 1≤t≤T, is received at input layer 710 across the time period from time step 1 to time step T (1≤t≤T). The word sequence {w_(t)} is thus expressed as a sequence of T words and is, for example, unstructured natural language text.

At the previous time step t−1, previous left context unit 714 receives a previous left word context s_(t−1) from left context unit 722 via left recurrent connection 740. At the current time step t, previous left context unit 714 of input layer 710 provides the previous left word context s_(t−1) to left context unit 722. The previous left word context s_(t−1) is the internal representation of context that is output from left context unit 722 of hidden layer 720 at the previous time step t−1. In particular, the previous left word context s_(t−1) is a vector representation of the portion of the word sequence received prior to time step t. For example, if the word sequence is “I'd really like a present for my birthday” and the current word being considered at time step t is “present,” then the previous left word context s_(t−1) would represent the portion “I'd really like a” that was received prior to time step t. Previous left word context s_(t−1) has a dimension of H in some examples.

At the subsequent time step t+1, subsequent right context unit 716 receives a subsequent right word context r_(t+1) from right context unit 724 via right recurrent connection 750. At the current time step t, subsequent right context unit 716 provides the subsequent right word context r_(t+1) to right context unit 724. The subsequent right word context r_(t+1) is the internal representation of context that is output from right context unit 724 of hidden layer 720 at the subsequent time step t+1. In particular, subsequent right word context r_(t+1) is a vector representation of the portion of the word sequence received after time step t. Returning to the previous example, if the current word being considered at time step t for the word sequence “I'd really like a present for my birthday” is “present,” the subsequent right word context r_(t+1) would represent the portion “for my birthday” that was received after time step t. Subsequent right word context r_(t+1) has a dimension of H in some examples. Notably, in some examples, the dimension H of previous left word context s_(t−1) is the same as the dimension H of subsequent right word context r_(t+1).

Left context unit 722 of hidden layer 720 receives the previous left word context s_(t−1) from previous left context unit 714 and the current word w_(t) from current word unit 712 and based on these received values, determines a left word context s_(t) for the current word w_(t). As described, in some examples, connections may be weighted. In this example, the connection between previous left context unit 714 and left context unit 722 is weighted by a weight factor (e.g., weight matrix) S and the connection between current word unit 712 and left context unit 722 is weighted by a weight factor W. Weight factor S and weight factor W are, for example, H×H and H×N dimension matrices, respectively.

Right context unit 724 of hidden layer 720 receives the subsequent right word context r_(t+1) from subsequent right context unit 716 and the current word w_(t) from current word unit 712 and based on these received values, determines a right word context r_(t) for the current word w_(t). In this example, the connection between subsequent right context unit 716 and right context unit 724 is weighted by a weight factor (e.g., weight matrix) R and the connection between current word unit 712 and right context unit 724 is weighted by the weight factor W. Weight factor R and weight factor W are, for example, H×H and H×N dimension matrices, respectively.

Accordingly, as shown below, left context unit 722 determines the left word context s_(t) in accordance with equation (1) and right context unit 724 determines the right word context s_(t) in accordance with equation (2): s _(t) =F{W·w _(t) +S·s _(t−1)}  (1) r _(t) =F{X·w _(t) +R·r _(t+1)}  (2) where F{ } denotes a function (e.g., activation function), such as a sigmoid function, a hyperbolic tangent function, a rectified linear unit function, any function related thereto, or any combination thereof. In some examples, the left word context s_(t) and the right word context r_(t) are each provided as a vector of dimension H. The left word context s_(t) can represent the forward state of the network at time step t based on the portion of the word sequence received from time step 1 to time step t. The right word context r_(t) can represent the current backward state of the network at time step t based on the portion of the word sequence received from time step t to time step T. In some examples, hidden layer 720 further determines the word-level feature representation h_(t) of the current word w_(t) by concatenating the left word context s_(t) and the right word context r_(t) to obtain word-level feature representation h_(t) of the current word w_(t). This determination is represented by equation (3) below: h _(t) =[s _(t) r _(t)]  (3) The word-level feature representation h_(t) of the current word w_(t) represents the current state of the network at time step t and is, for example, a vector of dimension 2H. It should be recognized that in some examples, the word-level feature representation h_(t) of the current word w_(t) can be determined by adding (rather than concatenating) the left word context s_(t) and the right word context r_(t). In these examples, the word-level feature representation h_(t) of the current word w_(t) is a vector of dimension H.

Hidden layer 720 provides the word-level feature representation h_(t) for the current word w_(t) to emoji label unit 732 of output layer 730, which based on the received representation, determines an emoji likelihood y_(t) for the current word w_(t). In some examples, the word-level feature representation h_(t) is provided to emoji label unit 732 using a connection weighted by a weight factor Y. Accordingly, emoji label unit 732 determines emoji likelihood y_(t) for the current word w_(t) in accordance with the equation (4) below: y _(t) =G{Y·h _(t))}  (4) where G{ } denotes a function, such as a softmax activation function. Generally, the emoji likelihood y_(t) for the current word w_(t) indicates the likelihood that the semantic meaning of the current word w_(t) corresponds to a respective emoji character. Thus, the emoji likelihood y_(t) for the current word w_(t) represents the likelihood that the corresponding emoji character should be presented for the current word w_(t) given the word sequence. In some examples, the emoji likelihood y_(t) includes the probability that a corresponding emoji character for the current word w_(t) should be presented (“YES” probability). In some examples, the emoji likelihood y_(t) for the current word w_(t) includes the probability that a corresponding emoji character for the current word w_(t) should not be presented (“NO” probability). Further, in some examples, the emoji likelihood y_(t) for the current word w_(t) includes the probability that the current word w_(t) does not correspond to any emoji character (“DON'T CARE” probability). Thus, depending on the implementation, the emoji likelihood y_(t) can be a vector of M×1 dimension, where M can be any value from 1 to 3 (e.g., any one of “YES,” “NO,” “DON'T CARE” probabilities, or combinations thereof). It should be recognized that in other implementations, the emoji likelihood y_(t) can include other probability labels (e.g., M can be greater than 3).

The determined emoji likelihood y_(t) can be used for emoji word sense disambiguation to ensure that the appropriate emoji character is presented for an intended semantic meaning of the current word. It should be recognized that similar determinations of left word context, right word context, word level feature representation, and emoji likelihood can be performed using EWSD model 700 for each respective word of the word sequence at each time step. Using the determined emoji likelihood for a respective word in the word sequence, the text input module (e.g., text input module 134) of the electronic device can determine whether a candidate predicted emoji should be presented for display given the text input of the word sequence.

Although EWSD model 700 only includes a single bi-directional hidden layer, it should be recognized that, in other examples, an arbitrarily complex, deeper network can be implemented. For example, EWSD model 700 can include two or more RNN/LSTM networks stacked on top of one another.

FIG. 8 illustrates EWSD model 800 in accordance with some embodiments. In particular, FIG. 8 illustrates an unfolded representation of EWSD model 800 for each time step (1≤t≤T). Like EWSD model 700, EWSD model 800 can serve to determine whether a corresponding emoji character should be presented for a given word of a word sequence. However, unlike EWSD model 700, EWSD model 800 further implements an attention mechanism (e.g., attention mechanism 840), which can improve its sensitivity toward detecting and accounting for context that is relevant to the semantic meaning of the current word. EWSD model 800 can be implemented using one or more multifunction devices, including, but not limited to, devices 100 and 300 (FIGS. 1A and 3). In some examples, EWSD model(s) 610 of text prediction module 600 includes EWSD model 800.

As shown, EWSD model 800 includes word-level feature extraction stage 810, semantic extraction stage 820, attention mechanism 840, and sequence labeling stage 850. Generally, word-level feature extraction stage 810 is configured to receive a word sequence and extract word-level feature representation for each word in the word sequence. Semantic extraction stage 820 is configured to combine, from word-level feature extraction stage 810, left word contexts and right word contexts for the words in the word sequence and determine a global semantic representation for the word sequence. Attention mechanism 840 is configured to compare the word-level feature representations from word-level feature extraction stage 810 with the global semantic representation from semantic extraction stage 820 and determine a respective attention coefficient for adjusting each word-level feature representation. Finally, sequence labeling stage 850 is configured to receive an adjusted word-level feature representation for each word of the word sequence and output a respective emoji likelihood. In some examples, the emoji likelihood for a given word in the word sequence indicates the likelihood that a corresponding emoji character should be presented for the given word.

In some examples, word-level feature extraction stage 810 is similar or identical to word-level feature extraction stage 760 of FIGS. 7A-7B, described above. As shown, word-level feature extraction stage 810 comprises a single bi-directional LSTM stage 812. It should be recognized that in other examples, word-level feature extraction stage 810 can comprise a stack of two or more bi-directional LSTM stages. LSTM stage 812 includes left context unit 814 and right context unit 816, which are similar or identical to left context unit 722 and right context unit 724, respectively, described above. In operation, word-level feature extraction stage 810 receives a word sequence {w_(t)} over a time period from time step t=1 to time step t=T. In particular, each word w_(t) of word sequence {w_(t)} is received at a respective time step t. For each word w_(t) of the word sequence, left context unit 814 receives previous left word context s_(t−1) for the word w_(t) from previous time step t−1 and determines the left word context s_(t) for the word w_(t) by applying the weight factors W and S. This determination is performed according to equation (1), described above. Similarly, for each word w_(t) of the word sequence, right context unit 816 receives subsequent right word context r_(t+1) for the word w_(t) from subsequent time step t+1 and determines the right word context r_(t) for the word w_(t) by applying the weight factors X and R. This determination is performed according to equation (2), described above. The left word context s_(t) and right word context r_(t) for each word w_(t) of the word sequence are then provided to concatenation unit 818, which determines the word-level feature representation h_(t) of the respective word w_(t). In particular, concatenation unit 818 determines word-level feature representation h_(t) of each word w_(t) in the word sequence by concatenating the respective left word context s_(t) and the respective right word context r_(t) according to equation (3), described above.

Semantic extraction stage 820 serves to extract suitable semantic information for the word sequence in an unsupervised fashion. In particular, semantic extraction stage 820 receives, from word-level feature extraction stage 760, the left word context s_(t) and the right word context r_(t) for each word w_(t) of the word sequence and combines them to determine a global semantic representation g for the word sequence. As shown in FIG. 8, semantic extraction stage 820 includes pooling stage 822, which includes pooling layer 824 and LSTM layer 826. Although in this example, semantic extraction stage 820 is depicted with only one pooling stage 822, it should be recognized that, in other examples, the semantic extraction stage can include a hierarchy of multiple stacked stages comprising LSTM layers interspersed with pooling layers.

Pooling layer 824 is configured to group the left word contexts s_(t) and the right word contexts r_(t) into K groups of left word contexts and K groups of right word contexts. Each group corresponds to T/K adjacent words of the word sequence. Forward portion 828 of pooling layer 824 combines each group of left word contexts to determine a respective pooled left context f_(k). Similarly, backward portion 830 of pooling layer 824 combines each group of right word contexts to determine a respective pooled right context b_(k). This determination is performed according to equations (5) and (6), described below:

$\begin{matrix} {f_{k} = {\frac{1}{\mathcal{J}_{k}}{\sum\limits_{i \in \mathcal{J}_{k}}s_{i}}}} & (5) \\ {b_{k} = {\frac{1}{\mathcal{J}_{k}}{\sum\limits_{i \in \mathcal{J}_{k}}r_{i}}}} & (6) \end{matrix}$ where Γ_(k) is the k^(th) instance of K non-overlapping subsets of [1 . . . T], each associated with a span of approximately [T/K] words. Pooled left context f_(k) represents the average left context for the respective k^(th) group of left word contexts. Similarly, pooled right context b_(k) represents the average right context for the respective k^(th) group of right word contexts. In the present example shown in FIG. 8, each group contains a pair of left word contexts or a pair of right word contexts corresponding to adjacent word pairs in the word sequence. Thus, in this example, K is equal to T/2. It should be recognized that, in other examples, left word contexts and right word contexts can be grouped according to any number of adjacent words in the word sequence.

Each pooled left context f_(k) and pooled right context b_(k) is provided to LSTM layer 826, which further distills the semantic features of the word sequence. LSTM layer 826 operates in a manner analogous to LSTM stage 812. In particular, LSTM layer 826 determines, using weight factors F and U, a forward pooled context u_(k) based on the respective pooled left context f_(k) and the previous k−1 instance of the forward pooled context u_(k−1). Similarly, LSTM layer 826 determines, using weight factors B and V, a backward pooled context v_(k) based on the respective pooled right context b_(k) and the subsequent k+1 instance of the backward pooled context v_(k+1). These determinations are performed according to equations (7) and (8), described below: u _(k) =F{F·f _(k) +U·u _(k−1)}  (7) v _(k) =F{B·b _(k) +V·v _(k+1)}  (8) where F{ } denotes a function (e.g., activation function), such as a sigmoid function, a hyperbolic tangent function, a rectified linear unit function, any function related thereto, or any combination thereof. As previously mentioned, multiple pooling stages comprising alternating pooling and LSTM layers can be stacked in a hierarchical manner in semantic extraction stage 820.

Final pooling stage 832 receives the forward pooled contexts {u_(k)} and the backward pooled contexts {v_(k)} from pooling stage 822 and determines a global forward pooled context u and a global backward pooled context v for the word sequence. The global forward pooled context u represents an average of the forward pooled contexts {u_(k)} and is determined according to equation (9) below. The global backward pooled context v represents an average of the backward pooled contexts {v_(k)} and is determined according to equation (10) below.

$\begin{matrix} {u = {\frac{1}{K}{\sum\limits_{k}u_{k}}}} & (9) \\ {v = {\frac{1}{K}{\sum\limits_{k}v_{k}}}} & (10) \end{matrix}$

The global forward pooled context u and the global backward pooled context v for the word sequence {w_(t)} are provided to global concatenation unit 834 to determine the global semantic representation g of the word sequence {w_(t)}. In particular, global concatenation unit 834 concatenates the global forward pooled context u and the global backward pooled context v according to equation (11) below to obtain the global semantic representation g of the word sequence {w_(t)}. g=[u v]  (11) In some examples, the global semantic representation g is a vector that encapsulates the semantic information contained in the entire word sequence {w_(t)}. Specifically, it is a representation of the relevant topic of discourse for the word sequence {w_(t)} as a whole.

The global semantic representation g serves as one of the inputs to attention mechanism 840, along with the word-level feature representation h_(t) of each word w_(t) of the word sequence {w_(t)}. In operation, attention mechanism 840 compares the global semantic representation g with the feature representation h_(t) of each word w_(t) to determine an attention coefficient a_(t) for each word w_(t). Specifically, the attention coefficient a_(t) for a respective word w_(t) is determined according to equation (12), described below: a _(t) =G{g ^(T) ·h _(t)}  (12) where G{ } denotes a function, such as a softmax activation function, and g^(T) is the transpose of the global semantic representation g. The attention coefficient a_(t) for a respective word w_(t) represents the semantic congruence between the global semantic representation g for the word sequence and the word-level feature representation h_(t) of the respective word w_(t). Therefore, the attention coefficient a_(t) can be higher for words that better reflect the overall topic of the word sequence {w_(t)}. In this way, words that are more salient for determining whether a corresponding emoji character should be presented would have higher attention coefficients, whereas words that are less salient to whether the corresponding emoji character should be presented would have lower attention coefficients.

The attention coefficient a_(t) for each respective word w_(t) is provided to adjusting unit 842. Adjusting unit 842 receives the word-level feature representations h_(t) of each respective word w_(t) from word-level feature extraction stage 810 and adjusts each word-level feature representation h_(t) by applying the respective attention coefficients a_(t) to each word-level feature representation h_(t). In particular, an adjusted feature representation h_(t)′ is determined for each respective word w_(t) by performing the element-wise product of the attention coefficient a_(t) with the word-level feature representation h_(t) of the respective word w_(t), as described in equation (13) below: h _(t) ′=a _(t) ·h _(t)  (13) Attention coefficient a_(t) thus functions as a weighting factor where word-level feature representations h_(t) that are more salient to the overall topic of the word sequence (and thus more pertinent to whether a corresponding emoji character should be presented) are weighted more significantly, whereas word-level feature representations h_(t) that are irrelevant to the overall topic of the word sequence (and thus less pertinent to whether a corresponding emoji character should be presented) are weighted less significantly. This improves the sensitivity, robustness, and accuracy for performing EWSD.

Finally, the adjusted feature representation h_(t)′ of each respective word w_(t) is provided to sequence labeling stage 850, which determines the respective emoji likelihood y_(t) for each respective word w_(t). Sequence labeling stage 850 is similar or analogous to emoji labeling stage 770, except that at sequence labeling stage 850, the emoji likelihood y_(t) is determine based on the adjusted feature representation h_(t)′ of a respective word w_(t) rather than the word-level feature representation h_(t) of a respective word w_(t). The determination is performed according to equation (14) below: y _(t) =G{Y·h _(t)′)}  (14)

In some examples, as discussed above, determining emoji likelihoods y_(t) using attention-gated states (adjusted word-level feature representations h_(t)′) instead of the original states (word-level feature representation h_(t)) can lead to more accurate emoji likelihoods. This improves the likelihood that a corresponding emoji character (e.g.,

) is presented for the relevant semantic meaning of a trigger word (e.g., “present”).

Although FIG. 8 is depicted as determining attention coefficients a_(t), adjusted feature representations h_(t)′, and emoji likelihoods y_(t) for every word in the word sequence, it should be recognized that, in some examples, the determination can be performed only for certain words in the word sequence. For instance, in some examples, the determination is performed only for words in the word sequence that correspond to predetermined trigger words in a list of predetermined trigger words. Each predetermined trigger word in the list of predetermined trigger words maps to a corresponding predetermined emoji character.

Returning to FIG. 6, text prediction module 600 outputs a plurality of candidate predicted words/emoji characters and corresponding likelihood scores, which include the predicted emoji characters and corresponding emoji likelihoods determined by EWSD model(s) 610. In some examples, text prediction module 600 provides the plurality of candidate predicted words/emoji characters and corresponding likelihood scores to text input module 134. In particular, text input module 134 ranks the plurality of candidate predicted words/emoji characters using the likelihood scores and selects the n-best (n-highest ranked) candidate predicted words/emoji characters to be presented for display, where n is a predetermined integer greater than zero. Specifically, with respect to predicted emoji characters, text input module 134 compares the respective emoji likelihoods from EWSD model(s) 610 and determines whether the emoji likelihoods satisfy one or more predetermined criteria. In response to determining that an emoji likelihood satisfies one or more predetermined criteria, the respective emoji character is caused to be presented for display.

In some examples, the n-best candidate predicted words/emoji characters are displayed on a text input interface (e.g., text prediction/correction interface). In response to receiving a user selection of a candidate predicted word/emoji via a text input interface, text input module 134 (in conjunction with touch screen 112, display controller 156, contact/motion module 130, and graphics module 132), causes the selected candidate predicted word/emoji character to be displayed on a user interface of the device (e.g., a text field displayed on touchscreen 112 of device 100). In other examples, text input module 134 causes one of the n-best candidate predicted words/emoji characters (e.g., the highest ranked predicted word/emoji character) to be displayed on the user interface without user selection. The candidate predicted word/emoji character, for example, supplements or replaces one or more words displayed on the user interface.

FIG. 9 is a flow diagram illustrating process 900 for emoji word sense disambiguation in accordance with some embodiments. Process 900 is performed, for example, using one or more electronic devices (e.g., 100, 300, or 500). Process 900 is further performed, for example, using an EWSD model (e.g., EWSD models 700 and/or 800 of FIGS. 7A-7B and 8) implemented on the one or more devices (e.g., in text prediction module 600 of the device). Operations in process 900 are, optionally, combined and/or the order of some operations is, optionally, changed. Further, some operations in process 900 are, optionally, omitted.

At block 902, text input is received. The text input is, for example, natural language input. The text input includes a word sequence. The words of the word sequence are received, for example, sequentially within a time period from time step t=1 to time step t=T. Each word or group of words in the word sequence corresponds to a respective time step tin the time period. In some examples, the word sequence is represented by the vector {w_(t)}, 1≤t≤T, where w_(t) is the word received at time step t. The text input is received, for example, at an ESWD model (e.g., EWSD model 700 or EWSD model 800). Specifically, the text input is received at the input layer (e.g., input layer 710) of the ESWD model. Examples A-D, described above, are exemplary natural language inputs that can be received at block 902.

In some examples, one or more words of the word sequence correspond to a predefined word of a predefined emoji character. In particular, each word of a list of predefined words (e.g., trigger words) is mapped to a corresponding predefined emoji character. For example, the list of predefined words can include the words “bicycle,” “telephone,” and “heart,” which correspond to the predefined emoji characters, “

,” “

,” and “

,” respectively. Further, in some examples, one or more words of the word sequence are homographs that each has a plurality of semantic meanings (also referred to as “senses”). For example, the one or more words of the word sequence can include the homograph “present,” “light,” “watch,” or “bow,” which corresponds to the predefined emoji characters “

” “

,” “

,” “

,” respectively. Notably, each homograph has only one particular meaning that coincides semantically with its respective emoji character. Specifically, “present,” the noun (meaning “gift”), coincides with the emoji character “

” and “bow,” the verb (meaning bending one's head or upper body), coincides with the emoji character “

.” As will become evident from the description below, process 900 can serve to disambiguate between the different semantic meanings of a homograph in the word sequence and determine whether the corresponding emoji character should be presented given the context of the word sequence surrounding the homograph.

At block 904, a determination is made as to whether one or more input conditions related to the text input is satisfied. In some examples, the one or more input conditions include the condition of not detecting any text input within a period of time that exceeds a predetermined threshold duration (e.g., 0.2, 0.5, or 0.7 seconds). For example, if the user types a word sequence via a keyboard interface of the device and then stops typing for more than a predetermined threshold duration, the one or more input conditions can be determined to be satisfied. In some examples, the one or more input conditions include the condition of detecting the input of a predetermined text character (e.g., a space or a punctuation character, such as a period, a comma, or the like) or a combination of text characters. In some examples, the one or more input conditions include the condition of detecting the user selection of a specific key on the keyboard, which, when selected, causes the display of the emoji keyboard (e.g., the globe icon key on the keyboard of iOS, the mobile operating system developed by Apple Inc.). A person of ordinary skill in the art should recognize that other input conditions can be implemented at block 904.

In response to determining that the one or more input conditions related to the text input are satisfied, one or more of blocks 906-924 (e.g., blocks 906-912) are performed. Conversely, in response to determining that the one or more input criteria related to the text input are not satisfied, process 900 ceases to continue processing the word sequence for emoji word sense disambiguation. Specifically, for example, process 900 forgoes performing blocks 906-924 and returns to block 902.

Implementing one or more input criteria (block 904) for performing EWSD can be advantageous to ensure that the word sequence contains sufficient context (e.g., a sufficient number of words) to effectively perform EWSD. With sufficient context being considered during EWSD computations, the relevancy and accuracy of predicted emoji characters being presented to the user can improve. Moreover, implementing input criteria can also be a more intelligent approach to initiating EWSD. In particular, the EWSD determinations of blocks 906-924 need not be initiated after each word received, but only periodically after detecting that a batch of words has been entered by the user. This can reduce the frequency at which EWSD computations are performed, which can reduce overall power consumption by the device. User experience can also improve because the user can be less frequently interrupted with predicted emoji characters being presented.

At block 906, a left word context s_(t) is determined for each respective word w_(t) of the word sequence. In particular, each left word context s_(t) is determined based on the respective word w_(t) and a respective previous left word context s_(t−1). The left word context s_(t) is, for example, a vector of dimension H. As described above, left word context s_(t) is determined, for example, by a left context unit (e.g., left context unit 722) of the ESWD model according to equation (1). In particular, the left word context s_(t) for the respective word w_(t) is determined by combining a vector representation for the respective word w_(t) with the respective previous left word context s_(t−1). The respective previous left word context s_(t−1) is, for example, a vector representation of a portion of the word sequence received prior to the respective word w_(t). For example, referring back to the natural language input of Example A above (“For my birthday present, what I'd really like is an Apple Watch!”), the left word context s_(t) for the word “present” is a vector representation of the words “For my birthday present” in the word sequence and the respective previous left word context s_(t−1) is a vector representation of the words “For my birthday” in the word sequence.

Determining the left word context s_(t) (block 906) can enable the EWSD process to account for the context in the word sequence received prior to the word w_(t), which can be advantageous for disambiguating the semantic meaning of the word w_(t). In particular, using the left word context s_(t) for EWSD can improve the accuracy of the emoji likelihood y_(t) and thus improve the robustness of emoji character prediction. For example, referring to the natural language inputs of Examples A, C, and D above, the left word context s_(t) can help to determine that the semantic meaning of the word “present” in Example A coincides with the corresponding emoji character “

” whereas the semantic meaning of the word “present” in Examples C and D do not coincide with the corresponding emoji character “

.”

At block 908, a right word context r_(t) is determined for each respective word w_(t) of the word sequence. In particular, each right word context r_(t) is determined based on the respective word w_(t) and a respective subsequent right word context r_(t+1). The right word context r_(t) is, for example, a vector of dimension H. Thus, in some examples, the left word context s_(t) and the right word context r_(t) are each vectors of the same dimension H. As described above, the right word context r_(t) is determined, for example, by the right context unit (e.g., right context unit 724) of the ESWD model according to equation (2). In particular, the right word context r_(t) for the respective word w_(t) is determined by combining a vector representation for the respective word w_(t) with the respective subsequent right word context r_(t+1). The respective subsequent right word context r_(t+1) is, for example, a vector representation of a portion of the word sequence received after the respective word w_(t). For example, referring back to the natural language input of Example A, the right word context r_(t) for the word “present” is a vector representation of the words “present, what I'd really like is an Apple Watch!” and the respective subsequent right word context r_(t+1) is a vector representation of the words “what I'd really like is an Apple Watch!” in the word sequence.

Determining the right word context r_(t) (block 908) can enable the EWSD process to account for the context in the word sequence received after the word w_(t), which can be advantageous for disambiguating the semantic meaning of the word w_(t). In particular, using the right word context r_(t) for EWSD can improve the accuracy of the emoji likelihood y_(t) and thus improve the robustness of emoji character prediction. For example, referring to the natural language input of Example B above, the right word context r_(t) can help to determine that the semantic meaning of the word “present” in Example B does not coincide with the corresponding emoji character “

.”

At block 910, a word-level feature representation h_(t) is determined for each respective word w_(t) of the word sequence. In particular, the word-level feature representation h_(t) of a respective word w_(t) is determined based on the left word context s_(t) for the respective word w_(t) and the right word context r_(t) for the respective word w_(t). The word-level feature representation h_(t) can represent the semantic and/or syntactic features of the respective word w_(t) given the context (both before and after) surrounding the respective word in the word sequence. In some examples, the word-level feature representation h_(t) is a vector of dimension 2H. Block 910 is performed, for example, by a hidden layer (e.g., hidden layer 720) or a concatenation unit (e.g., concatenation unit 818) of the ESWD model. In some examples, the word-level feature representation h_(t) of the respective word w_(t) is determined by concatenating the left word context s_(t) for the respective word w_(t) and the right word context r_(t) for the respective word w_(t). Specifically, the word-level feature representation h_(t) of the respective word w_(t) is determined according to equation (3), described above.

Determining the word-level feature representation h_(t) (block 908) can enable the EWSD process to account for the surrounding context of word w_(t) (both before and after word w_(t)), which can be advantageous for disambiguating the semantic meaning of the word w_(t). In particular, the word-level feature representation h_(t) is a combination of the left word context s_(t) and right word context r_(t) and thus accounts for the entire context in the word sequence that surrounds the word w_(t) (rather than only a small window of m words before and after the word w_(t)). Thus, using the word-level feature representation h_(t) for EWSD can improve the accuracy of the emoji likelihood y_(t) and improve the robustness of emoji character prediction.

At block 912, a global semantic representation g for the word sequence is determined. In particular, the global semantic representation g for the word sequence is determined based on the left word context s_(t) and the right word context r_(t) for each respective word r_(t) of the word sequence. The global semantic representation g for the word sequence can represent the overall semantic features of the entire word sequence. Block 912 is performed, for example, by a semantic extraction stage (e.g., semantic extraction stage 820) of the ESWD model.

In some examples, the global semantic representation g for the word sequence is determined by combining and/or averaging the left word contexts {s_(t)} and the right word contexts {r_(t)} for the words in the word sequence to obtain a single vector (g). The left word contexts {s_(t)} and the right word contexts {r_(t)} are combined, for example, using a hierarchy of stages (e.g., pooling stage(s) 822) that includes at least one pooling layer (e.g., pooling layer 824) and at least one LSTM network layer (e.g., LSTM layer 826). A pooling layer of the at least one pooling layer combines respective left word contexts s_(t) for groups of adjacent words in the word sequence to obtain a respective pooled left context f_(k) for each group of adjacent words (e.g., according to equation (5), described above). The pooling layer also combines respective right word contexts r_(t) for the groups of adjacent words to obtain a respective pooled right context b_(k) for each group of adjacent words (e.g., according to equation (6), described above). For example, referring to the word sequence of Example A above, the pooling layer can combine respective left word contexts s_(t) for each of the six following adjacent word pairs: “For my,” “birthday present,” “what I'd,” “really like,” “is an,” “Apple Watch!” to obtain six respective pooled left contexts f_(k). Similarly, the pooling layer can combine respective right word contexts r_(t) for each of the same six adjacent word pairs to obtain six respective pooled right contexts b_(k). Although, in this example, the left word contexts and the rights word contexts are combined according to adjacent word pairs of the word sequence, it should be recognized that in other examples, the left word contexts and the rights word contexts can be combined according to any number of adjacent words (e.g., three consecutive words or four consecutive words) in the word sequence.

The pooling layer provides the pooled left contexts {f_(k)} and the pooled right contexts {b_(k)} to the LSTM network layer 826, which determines respective forward pooled contexts u_(k) and respective backward pooled contexts v_(k) according to equations (7) and (8), described above. Specifically, a forward pooled context u_(k) for a respective pooled left context f_(k) is determined based on the respective pooled left context f_(k) and the previous k−1 instance of the forward pooled context u_(k−1), according to equation (7). Similarly, a backward pooled context v_(k) for a respective pooled right context b_(k) is determined based on the respective pooled right context b_(k) and the subsequent k+1 instance of the backward pooled context v_(k+1) according to equation (8).

It should be recognized that any number of pooling layers and LSTM network layers can be stacked in a hierarchical and alternating manner to combine the left word contexts {s_(t)} and the right word contexts {r_(t)} for the words in the word sequence. The forward pooled contexts {u_(k)} and the backward pooled contexts {v_(k)} obtained at the final LSTM network stage can be provided to a final pooling stage (final pooling stage 832), which combines and/or averages the forward pooled contexts {u_(k)} and the backward pooled contexts {v_(k)} to determine a respective global forward pooled context u and a global backward pooled context v for the word sequence. The global forward pooled context u represents an average of the forward pooled contexts {u_(k)} and is determined according to equation (9), described above. The global backward pooled context v represents an average of the backward pooled contexts {v_(k)} and is determined according to equation (10), described above.

The global semantic representation g for the word sequence is determined from the global forward pooled context u and the global backward pooled context v for the word sequence (e.g., using global concatenation unit 834). In particular, the global forward pooled context u and the global backward pooled context v are concatenated according to equation (11) to determine the global semantic representation g of the word sequence.

Determining the global semantic representation g for the word sequence can serve to distill down the word-level semantic information in the left word contexts {s_(t)} and the right word contexts {r_(t)} to obtain the overall relevant topic of discourse for the word sequence as a whole. The global semantic representation g can thus serve as a suitable reference point to enable the EWSD process to focus on words (e.g., using an attention mechanism) in the word sequence that are particularly salient to the overall topic and thus particularly relevant to predicting emoji characters for display. This improves the accuracy of determining the emoji likelihood and increases the robustness of emoji character prediction.

Blocks 914-924 generally relate to comparing the word-level feature representations h_(t) of words in the word sequence with the global semantic representation g for the word sequence and adjusting the word-level representations h_(t) based on the comparison. Emoji likelihoods y_(t) are determined based on the adjusted word-level feature representations h_(t)′. The emoji likelihoods y_(t) are then used to determine whether emoji characters corresponding to the respective words in the word sequence should be presented for display.

In some examples, blocks 914-924 are performed for each and every word in the word sequence. In other examples, blocks 914-924 are performed only for words in the word sequence that correspond to predefined words of predefined emoji characters. Specifically, in these examples, each word in the word sequence is compared to a list of predefined words, where each word in the list of predefined words is mapped to a corresponding predefined emoji character (e.g., “bicycle,” “telephone,” and “heart,” mapped to emoji characters, “

,” “

,” and “

”). Blocks 914-924 are then performed for each word in the word sequence that matches a word in the list of predefined words. For example, with reference to the word sequence of Example A above, the words “birthday,” “present,” “Apple,” “Watch” each correspond to a predefined word of a predefined emoji character (

,

,

,

, respectively). Thus, in this example, blocks 914-924 are performed for only these words.

At block 914, an attention coefficient a_(t) is determined for a respective word w_(t) in the word sequence. In particular, the attention coefficient a_(t) is determined based on a congruence between the word-level feature representation h_(t) of the respective word w_(t) and the global semantic representation g for the word sequence (e.g., according to equation (12), described above). Thus, the attention coefficient a_(t) represents the semantic congruence between the word sequence and the respective word w_(t). Words that are more salient to the overall semantic meaning of the word sequence would have a higher attention coefficient a_(t) (and vice versa). Block 914 is performed, for example, using an attention mechanism (attention mechanism 840) of an EWSD model.

Determining the attention coefficient a_(t) for a respective word w_(t) enables the EWSD process to quantify the salience of the respective word w_(t) to the overall topic of the word sequence and thus its relevance for predicting emoji characters. Using the determined attention coefficient a_(t), words that are less relevant can contribute less to determining the emoji likelihood, whereas words that are more relevant can contribute more to determining the emoji likelihood. This improves the accuracy of determining the emoji likelihood and increases the robustness of emoji character prediction.

At block 916, the word-level feature representation h_(t) of the respective word w_(t) in the word sequence is adjusted to determine an adjusted feature representation h_(t)′ of the respective word w_(t). In particular, the word-level feature representation h_(t) of the respective word w_(t) is adjusted based on the attention coefficient a_(t) for the respective word w_(t) (e.g., according to equation (13), described above). The attention coefficient a_(t) thus serves as a weighting factor to determine the adjusted feature representation h_(t)′ of the respective word w_(t). Block 916 is performed, for example, using an adjusting unit (e.g., adjusting unit 842) of an EWSD model.

Adjusting the feature representation h_(t) of the respective word w_(t) can tailor the contribution of the respective word w_(t) to the emoji likelihood according to its relevance for predicting emoji characters. This improves the accuracy of determining the emoji likelihood and increases the robustness of emoji character prediction.

At block 918, an emoji likelihood y_(t) for the respective word w_(t) is determined. In some examples, the emoji likelihood y_(t) is determined based on the adjusted feature representation h_(t)′ of the respective word (e.g., according to equation (14), described above). In these examples, block 918 is performed using a sequence labeling stage (e.g., sequence labeling stage 850) of an EWSD model (e.g., EWSD model 800). In other examples, the emoji likelihood y_(t) is determined based on the word-level feature representation h_(t) of the respective word w_(t) (e.g., according to equation (4), described above). In these examples, block 918 is performed using a sequence labeling stage (e.g., emoji label unit 732) of an EWSD model (e.g., EWSD model 700).

As discussed above, the emoji likelihood y_(t) indicates the likelihood that a corresponding emoji character should be presented for the respective word w_(t). The emoji likelihood y_(t) includes, for example, probability values for one or more labels (e.g., “YES,” “NO,” “DON'T CARE, or any combination thereof). The probability values for the one or more labels indicate how well the semantic meaning of the respective word matches the corresponding emoji character for the respective word.

Determining the emoji likelihood y_(t) for a respective word w_(t) can provide a more intelligent approach to predicting emoji characters. In contrast to conventional prediction solutions that automatically present emoji characters upon detecting trigger words without regard to the intended semantic meaning of the trigger words, the determined emoji likelihood y_(t) accounts for the context, semantic meaning, and salience of the trigger words so that emoji characters can be intelligently predicted. This improves the accuracy and robustness of emoji character prediction.

Referring back to the exemplary natural language input of Example B (“Regarding the best present, what I'd really like is an Apple Watch for my birthday!”), the emoji likelihood for the word “present” would indicate that the corresponding emoji character “

” should be presented. For example, the emoji likelihood for the word “present” in Example B would include a high probability value for the label “YES” and low probability values for the labels “NO” and “DON'T CARE.” In some cases, the emoji likelihoods for inconsequential words such as “the,” “what,” or the like in Example B would include a high probability value for the label “DON'T CARE” and low probability values for the labels “YES” and “NO.”

Referring now to the exemplary natural language input of Example D (“The past and the future are tiny matters compared to the present”), the emoji likelihood for the word “present” would indicate that the corresponding emoji character “

” should not be presented. For example, the emoji likelihood for the word “present” in Example D would include a high probability value for the label “NO” and low probability values for the labels “YES” and “DON'T CARE.”

At block 920, a determination is made as to whether the emoji likelihood y_(t) of the respective word w_(t) satisfies one or more criteria. The one or more criteria include, for example, one or more predetermined conditions (e.g., predetermined rules). In a specific example, the one or more criteria include the criteria that the probability value for a first label (e.g., “YES”) exceeds a first predetermined threshold value. Block 920 is performed, for example, using a text input module (e.g., text input module 134) of the device. The one or more criteria can include the additional criteria that the probability value for a second label (e.g., “NO” or “DON'T CARE”) is less than a second predetermined threshold value. In accordance with determining that the emoji likelihood w_(t) satisfies the one or more criteria, block 922 is performed. In accordance with determining that the emoji likelihood w_(t) is not satisfying the one or more criteria, block 924 is performed.

Determining whether the emoji likelihood y_(t) for a respective word w_(t) satisfies one or more criteria can serve to utilize the information provided by the emoji likelihood y_(t) to more intelligently predict emoji characters. The one or more criteria can thus filter out situations where the semantic meaning of the trigger word does not coincide with the corresponding emoji character. In this way, the emoji characters that are presented can more likely be relevant to the context of the trigger words, which increases the accuracy and robustness of emoji character prediction and improves user experience.

Returning to the exemplary natural language input of example B, the emoji likelihood for the word “present” would satisfy the one or more criteria at block 920. In contrast, for the exemplary natural language input of example D, the emoji likelihood for the word “present” would not satisfy the one or more criteria at block 920.

At block 922, an emoji character corresponding to the respective word of the word sequence is caused to be presented for display. In some examples, the emoji character corresponding to the respective word of the word sequence is an ideogram. Block 922 is performed, for example, using a text input module (e.g., text input module 134) of the device.

For the word “present” in the word sequence of Example B, the emoji character “

” can be caused to be presented for display at block 922. In some examples, the emoji character is displayed for user selection. In particular, the emoji character is displayed on a text input interface. In these examples, upon user selection of the displayed emoji character, the emoji character is either inserted into the word sequence on the display or the respective word is replaced with the emoji character. In other examples, the emoji character is automatically inserted into the word sequence or automatically replaces the respective word in the word sequence without requiring user selection. Examples of the emoji character “

” corresponding to the word “present” being inserted and replaced for the word sequence of Example B is shown below:

“Regarding the best present

, what I'd really like is an Apple Watch for my birthday!”

“Regarding the best

, what I'd really like is an Apple Watch for my birthday!”

At block 924, process 900 forgoes causing the emoji character corresponding to the respective word to be presented for display. Specifically, the intended semantic meaning of the trigger word can be determined not to coincide with the corresponding emoji character and thus the emoji character is not predicted. For instance, referring to the exemplary natural language input of Example D, process 900 forgoes causing the emoji character “

” to be presented for the word “present” since the “present” in the context of the entire input means a time period rather than a gift.

The operations described above with reference to FIGS. 9A-9B are, optionally, implemented by components depicted in FIG. 1A-1B, 3, or 6. For example, receiving operation 902, determining operations 904, 908, 910, 912, 914, 918, and 920, adjusting operation 916, causing operation 922, and forgoing operation 924 are, optionally, implemented by text input module 134, text prediction engine 602, EWSD model(s) 610, or any combination thereof. Similarly, it would be clear to a person having ordinary skill in the art how other processes can be implemented based on the components depicted in FIGS. 1A-1B, 3, and 6.

In accordance with some implementations, a computer-readable storage medium (e.g., a non-transitory computer readable storage medium) is provided, the computer-readable storage medium storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises means for performing any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises a processing unit configured to perform any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises one or more processors and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods or processes described herein.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various embodiments with various modifications as are suited to the particular use contemplated.

Although the disclosure and examples have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims. 

What is claimed is:
 1. An electronic device, comprising: one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for: receiving natural language input comprising a word sequence; for each respective word of the word sequence, determining a feature representation of the respective word based on a left word context for the respective word and the right word context for the respective word; determining a global semantic representation for the word sequence based on the left word context and the right word context for each respective word of the word sequence; and for each respective word of the word sequence that corresponds to a predefined word of a predefined emoji character: determining a respective attention coefficient based on a congruence between the feature representation of the respective word of the word sequence and the global semantic representation for the word sequence; adjusting the feature representation of the respective word of the word sequence based on the determined respective attention coefficient; determining a respective emoji likelihood based on the adjusted feature representation of the respective word of the word sequence; and in accordance with the respective emoji likelihood satisfying one or more criteria, causing an emoji character corresponding to the respective word of the word sequence to be presented for display.
 2. The device of claim 1, wherein a word of the word sequence that corresponds to a predefined word of a predefined emoji character has a plurality of semantic meanings.
 3. The device of claim 1, wherein the one or more programs further include instructions for: for each respective word of the word sequence, determining the left word context for the respective word based on the respective word and a respective previous left word context.
 4. The device of claim 3, wherein for each respective word of the word sequence, the left word context for the respective word is determined by combining a vector representation for the respective word with the respective previous left word context.
 5. The device of claim 3, wherein for each respective word of the word sequence, the respective previous left word context comprises a vector representation of a portion of the word sequence received prior to the respective word.
 6. The device of claim 1, wherein the one or more programs further include instructions for: for each respective word of the word sequence, determining the right word context for the respective word based on the respective word and a respective subsequent right word context.
 7. The device of claim 6, wherein for each respective word of the word sequence, the right word context for the respective word is determined by combining a vector representation for the respective word with the respective subsequent right word context.
 8. The device of claim 1, wherein for each respective word of the word sequence, the respective subsequent right word context comprises a vector representation of a portion of the word sequence received after the respective word.
 9. The device of claim 1, wherein for each respective word of the word sequence, the left word context and the right word context are each vectors of a same dimension.
 10. The device of claim 1, wherein for each respective word of the word sequence, the feature representation of the respective word is determined by concatenating or adding the left word context for the respective word and the right word context for the respective word.
 11. The device of claim 1, wherein the global semantic representation for the word sequence is determined using a hierarchy of stages comprising at least one pooling layer and at least one long short-term memory (LSTM) network layer.
 12. The device of claim 11, wherein a pooling layer of the at least one pooling layer is configured to: combine respective left word contexts for adjacent word pairs in the word sequence; and combine respective right word contexts for adjacent word pairs in the word sequence.
 13. The device of claim 1, wherein the respective attention coefficient for a respective word of the word sequence represents a degree of semantic similarity between the word sequence and the respective word.
 14. The device of claim 1, wherein the respective attention coefficient for a respective word of the word sequence is determined based on a product of a transpose of the global semantic representation for the word sequence and the feature representation of the respective word.
 15. The device of claim 1, wherein the emoji character corresponding to the respective word of the word sequence is an ideogram.
 16. The device of claim 1, wherein the one or more programs further include instructions for: determining whether one or more input criteria related to the natural language input is satisfied, wherein the feature representation of each respective word of the word sequence is determined in response to determining that the one or more input criteria related to the natural language input is satisfied.
 17. A method for emoji word sense disambiguation, the method comprising: at an electronic device: receiving natural language input comprising a word sequence; for each respective word of the word sequence, determining a feature representation of the respective word based on a left word context for the respective word and the right word context for the respective word; determining a global semantic representation for the word sequence based on the left word context and the right word context for each respective word of the word sequence; and for each respective word of the word sequence that corresponds to a predefined word of a predefined emoji character: determining a respective attention coefficient based on a congruence between the feature representation of the respective word of the word sequence and the global semantic representation for the word sequence; adjusting the feature representation of the respective word of the word sequence based on the determined respective attention coefficient; determining a respective emoji likelihood based on the adjusted feature representation of the respective word of the word sequence; and in accordance with the respective emoji likelihood satisfying one or more criteria, causing an emoji character corresponding to the respective word of the word sequence to be presented for display.
 18. The method of claim 17, wherein a word of the word sequence that corresponds to a predefined word of a predefined emoji character has a plurality of semantic meanings.
 19. The method of claim 17, further comprising: for each respective word of the word sequence, determining the left word context for the respective word based on the respective word and a respective previous left word context.
 20. The method of claim 19, wherein for each respective word of the word sequence, the left word context for the respective word is determined by combining a vector representation for the respective word with the respective previous left word context.
 21. The method of claim 19, wherein for each respective word of the word sequence, the respective previous left word context comprises a vector representation of a portion of the word sequence received prior to the respective word.
 22. The method of claim 17, further comprising: for each respective word of the word sequence, determining the right word context for the respective word based on the respective word and a respective subsequent right word context.
 23. The method of claim 22, wherein for each respective word of the word sequence, the right word context for the respective word is determined by combining a vector representation for the respective word with the respective subsequent right word context.
 24. The method of claim 17, wherein for each respective word of the word sequence, the respective subsequent right word context comprises a vector representation of a portion of the word sequence received after the respective word.
 25. The method of claim 17, wherein for each respective word of the word sequence, the left word context and the right word context are each vectors of a same dimension.
 26. The method of claim 17, wherein for each respective word of the word sequence, the feature representation of the respective word is determined by concatenating or adding the left word context for the respective word and the right word context for the respective word.
 27. The method of claim 17, wherein the global semantic representation for the word sequence is determined using a hierarchy of stages comprising at least one pooling layer and at least one long short-term memory (LSTM) network layer.
 28. The method of claim 27, wherein a pooling layer of the at least one pooling layer configured to: combine respective left word contexts for adjacent word pairs in the word sequence; and combine respective right word contexts for adjacent word pairs in the word sequence.
 29. The method of claim 17, wherein the respective attention coefficient for a respective word of the word sequence represents a degree of semantic similarity between the word sequence and the respective word.
 30. The method of claim 17, wherein the respective attention coefficient for a respective word of the word sequence is determined based on a product of a transpose of the global semantic representation for the word sequence and the feature representation of the respective word.
 31. The method of claim 17, wherein the emoji character corresponding to the respective word of the word sequence is an ideogram.
 32. The method of claim 17, further comprising: determining whether one or more input criteria related to the natural language input satisfied, wherein the feature representation of each respective word of the word sequence is determined in response to determining that the one or more input criteria related to the natural language input is satisfied.
 33. A non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of an electronic device with a display and memory, the one or more programs including instructions for: receiving natural language input comprising a word sequence; for each respective word of the word sequence, determining a feature representation of the respective word based on a left word context for the respective word and the right word context for the respective word; determining a global semantic representation for the word sequence based on the left word context and the right word context for each respective word of the word sequence; and for each respective word of the word sequence that corresponds to a predefined word of a predefined emoji character: determining a respective attention coefficient based on a congruence between the feature representation of the respective word of the word sequence and the global semantic representation for the word sequence; adjusting the feature representation of the respective word of the word sequence based on the determined respective attention coefficient; determining a respective emoji likelihood based on the adjusted feature representation of the respective word of the word sequence; and in accordance with the respective emoji likelihood satisfying one or more criteria, causing an emoji character corresponding to the respective word of the word sequence to be presented for display.
 34. The computer-readable storage medium of claim 33, wherein a word of the word sequence that corresponds to a predefined word of a predefined emoji character has a plurality of semantic meanings.
 35. The computer-readable storage medium of claim 33, wherein the one or more programs further include instructions for: for each respective word of the word sequence, determining the left word context for the respective word based on the respective word and a respective previous left word context.
 36. The computer-readable storage medium of claim 35, wherein for each respective word of the word sequence, the left word context for the respective word is determined by combining a vector representation for the respective word with the respective previous left word context.
 37. The computer-readable storage medium of claim 35, wherein for each respective word of the word sequence, the respective previous left word context comprises a vector representation of a portion of the word sequence received prior to the respective word.
 38. The computer-readable storage medium of claim 33, wherein the one or more programs further include instructions for: for each respective word of the word sequence, determining the right word context for the respective word based on the respective word and a respective subsequent right word context.
 39. The computer-readable storage medium of claim 38, wherein for each respective word of the word sequence, the right word context for the respective word is determined by combining a vector representation for the respective word with the respective subsequent right word context.
 40. The computer-readable storage medium of claim 33, wherein for each respective word of the word sequence, the respective subsequent right word context comprises a vector representation of a portion of the word sequence received after the respective word.
 41. The computer-readable storage medium of claim 33, wherein for each respective word of the word sequence, the left word context and the right word context are each vectors of a same dimension.
 42. The computer-readable storage medium of claim 33, wherein for each respective word of the word sequence, the feature representation of the respective word is determined by concatenating or adding the left word context for the respective word and the right word context for the respective word.
 43. The computer-readable storage medium of claim 33, wherein the global semantic representation for the word sequence is determined using a hierarchy of stages comprising at least one pooling layer and at least one long short-term memory (LSTM) network layer.
 44. The computer-readable storage medium of claim 43, wherein a pooling layer of the at least one pooling layer is configured to: combine respective left word contexts for adjacent word pairs in the word sequence; and combine respective right word contexts for adjacent word pairs in the word sequence.
 45. The computer-readable storage medium of claim 33, wherein the respective attention coefficient for a respective word of the word sequence represents a degree of semantic similarity between the word sequence and the respective word.
 46. The computer-readable storage medium of claim 33, wherein the respective attention coefficient for a respective word of the word sequence is determined based on a product of a transpose of the global semantic representation for the word sequence and the feature representation of the respective word.
 47. The computer-readable storage medium of claim 33, wherein the emoji character corresponding to the respective word of the word sequence is an ideogram.
 48. The computer-readable storage medium of claim 33, wherein the one or more programs further include instructions for: determining whether one or more input criteria related to the natural language input is satisfied, wherein the feature representation of each respective word of the word sequence is determined in response to determining that the one or more input criteria related to the natural language input is satisfied. 